diff --git a/docs/examples/DP_mixing.py b/docs/examples/DP_mixing.py
new file mode 100644
index 00000000..189d27f0
--- /dev/null
+++ b/docs/examples/DP_mixing.py
@@ -0,0 +1,119 @@
+"""
+This file contains the implementation of the EPPF induced by the Dirithclet process (DP)
+introduced in Ferguson (1973), see also Sethuraman (1994).
+The EPPF induced by the DP depends on a `totalmass` parameter M.
+Given a clustering of n elements into k clusters, each with cardinality
+n_j (j = 1, ..., k) the EPPF of the DP gives the following
+probabilities for the cluster membership of the (n+1)-th observation:
+
+   p(j-th cluster | ...) = n_j / (n + M)
+   p(new cluster | ...) = M / (n + M)
+
+The state is solely composed of M, but we also store log(M) for efficiency
+reasons. For more information about the class, please refer instead to base
+classes, `AbstractMixing` and `BaseMixing`.
+"""
+
+import numpy as np
+
+
+def initialize_state():
+    """ Initializing the state (total mass parameter M)
+
+    Returns
+    -------
+    float
+        initial value of the total mass parameter M
+    """
+    total_mass = 5
+    return [total_mass]
+
+
+def update_state(state, prior_values, allocation_size, unique_values):
+    """ Updating the state (total mass parameter M)
+
+    Returns
+    -------
+    float
+        updated value of the total mass parameter M
+    """
+    return state
+
+
+def mass_existing_cluster(n, n_clust, log, propto, hier_card, state):
+    """ Returns probability mass for an old cluster (for marginal mixings only)
+
+    Parameters
+    ----------
+    n : int
+        Total dataset size
+    n_clust : int
+        Number of clusters
+    log : bool
+        Whether to return logarithm-scale values or not
+    propto : bool
+        Whether to include normalizing constants or not
+    hier_card : int
+        Number of data points assigned to the `Hierarchy` object representing the cluster
+    state : :obj:`list` of :obj:`float`
+        State (total mass parameter M) values for each cluster
+
+    Returns
+    -------
+    float
+        probability mass
+    """
+    total_mass = state[0]
+    if log:
+        out = np.log(hier_card)
+        if not propto:
+            out -= np.log(n + total_mass)
+    else:
+        out = hier_card
+        if not propto:
+            out /= (n + total_mass)
+    return out
+
+
+def mass_new_cluster(n, n_clust, log, propto, state):
+    """ Returns probability mass for a new cluster (for marginal mixings only)
+
+    Parameters
+    ----------
+    n : int
+        Total dataset size
+    n_clust : int
+        Number of clusters
+    log : bool
+        Whether to return logarithm-scale values or not
+    propto : bool
+        Whether to include normalizing constants or not
+    state : :obj:`list` of :obj:`float`
+        State (total mass parameter M) values for each cluster
+
+    Returns
+    -------
+    float
+        probability mass
+    """
+    total_mass = state[0]
+    if log:
+        out = np.log(total_mass)
+        if not propto:
+            out -= np.log(n + total_mass)
+    else:
+        out = total_mass
+        if not propto:
+            out /= (n + total_mass)
+    return out
+
+
+def is_conditional():
+    """
+
+    Returns
+    -------
+    :bool:
+        True for conditional, False for marginal mixings
+    """
+    return False
diff --git a/docs/examples/README.md b/docs/examples/README.md
index accddda5..f45d250e 100644
--- a/docs/examples/README.md
+++ b/docs/examples/README.md
@@ -12,4 +12,13 @@ you can find examples in  ```docs/examples```. Specifically:
   initialize_hypers, update_hypers, draw, compute_posterior_hypers,
   update_summary_statistics```. Please refer to the ```NNIG_Hierarchy_NGG.py``` examples for details.
 
-For an example of how to run please refer to ```test_run.py```, ```estimate_pyhier_desnity.ipynb```.
\ No newline at end of file
+## To implement a mixing in Python
+Create a ```.py``` file in ```pybmix/docs/examples``` implementing all the necessary methods of the mixing,
+you can find an example in  ```docs/examples```. Specifically:
+- to implement a non-conditional mixing you need to define the methods:
+  ```is_conditional, update_state, initialize_state, mass_existing_cluster, mass_new_cluster```.
+  Please refer to the ```DP_mixing.py``` example for details.
+- to implement a conditional mixing you need to define the methods:
+  ```is_conditional, update_state, initialize_state, mixing_weights```.
+
+For working examples please refer to ```test_run.py``` and ```estimate_py_density.ipynb```.
\ No newline at end of file
diff --git a/docs/examples/README.rst b/docs/examples/README.rst
new file mode 100644
index 00000000..9feb4505
--- /dev/null
+++ b/docs/examples/README.rst
@@ -0,0 +1,28 @@
+To implement a hierarchy in Python
+----------------------------------
+
+Create a ``.py`` file implementing all the necessary methods of the
+hierarchy, you can find examples in ``docs/examples``. Specifically:
+
+-  to implement a non-conjugate hierarchy you need to define the
+   methods:
+   ``is_conjugate, like_lpdf, initialize_state, initialize_hypers,   update_hypers, draw, update_summary_statistics, sample_full_cond``.
+   Please refer to the ``LapNIG_Hierarchy.py`` example for details.
+-  to implement a conjugate hierarchy you need to define the methods:
+   ``is_conjugate, like_lpdf, marg_lpdf, initialize_state,   initialize_hypers, update_hypers, draw, compute_posterior_hypers,   update_summary_statistics``.
+   Please refer to the ``NNIG_Hierarchy_NGG.py`` examples for details.
+
+To implement a mixing in Python
+-------------------------------
+
+Create a ``.py`` file in ``pybmix/docs/examples`` implementing all the
+necessary methods of the mixing, you can find an example in
+``docs/examples``. Specifically: - to implement a non-conditional mixing
+you need to define the methods:
+``is_conditional, update_state, initialize_state, mass_existing_cluster, mass_new_cluster``.
+Please refer to the ``DP_mixing.py`` example for details. - to implement
+a conditional mixing you need to define the methods:
+``is_conditional, update_state, initialize_state, mixing_weights``.
+
+For working examples please refer to ``test_run.py`` and
+``estimate_pyhier_desnity.ipynb``.
diff --git a/docs/examples/compare_cpp_and_py.ipynb b/docs/examples/compare_cpp_and_py.ipynb
new file mode 100644
index 00000000..1fa275a7
--- /dev/null
+++ b/docs/examples/compare_cpp_and_py.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as ss\n",
+    "import sys\n",
+    "sys.path.append(\"../../\")\n",
+    "from pybmix.core.mixing import PythonMixing, DirichletProcessMixing\n",
+    "from pybmix.core.hierarchy import PythonHierarchy, UnivariateNormal\n",
+    "from pybmix.core.mixture_model import MixtureModel\n",
+    "from pybmix.estimators.density_estimator import DensityEstimator"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "np.random.seed(2022)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Generate data as mixture of two Normals\n",
+    "\n",
+    "$$\n",
+    "y_i \\sim \\frac{7}{10} \\mathcal N(-3, 1) + \\frac{3}{10} \\mathcal N(3, 1), \\quad i=1, \\ldots, 200\n",
+    "$$"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "def sample_from_mixture_norm(weights, means, sds, n_data):\n",
+    "    n_comp = len(weights)\n",
+    "    clus_alloc = np.random.choice(np.arange(n_comp), p=weights, size=n_data)\n",
+    "    return np.random.normal(loc=means[clus_alloc], scale=sds[clus_alloc])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_norm = sample_from_mixture_norm(\n",
+    "    np.array([0.7, 0.3]), np.array([-3, 3]), np.array([1, 1]), 200)\n",
+    "plt.hist(y_norm, bins=20)\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## NNIG Hierarchy and DP Mixing in Python"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "mixing_py = PythonMixing(\"DP_mixing\")\n",
+    "hierarchy_py = PythonHierarchy(\"NNIG_Hierarchy_fixed_values\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [
+    "mixture_py = MixtureModel(mixing_py, hierarchy_py)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [],
+   "source": [
+    "niter = 500\n",
+    "nburn = 100\n",
+    "algo = \"Neal2\""
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using hierarchy implementation in NNIG_Hierarchy_fixed_values.py\n",
+      "Using mixing implementation in: DP_mixing.py\n",
+      "Initializing... Done\n",
+      "Running Neal2 algorithm with PythonHier hierarchies, PythonMix mixing...\n",
+      "[============================================================] 100% 88.155s\r===========>                                               ] 20% 19.482s[============>                                               ] 20% 19.86s[============>                                               ] 21% 20.094s[=============>                                              ] 21% 20.477s[=============>                                              ] 22% 20.883s[=============>                                              ] 22% 21.31s[=============>                                              ] 23% 21.651s[==============>                                             ] 23% 21.999s[==============>                                             ] 23% 22.357s[==============>                                             ] 24% 22.757s[==============>                                             ] 24% 23.12s[===============>                                            ] 25% 23.429s[===============>                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87.244s[===========================================================>] 99% 87.643s[===========================================================>] 99% 87.97s[============================================================] 100% 88.156s\r\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "mixture_py.run_mcmc(y_norm, algorithm=algo, niter=niter, nburn=nburn)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[============================================================] 100% 64.753s==================>                                         ] 30% 20.093s[==================>                                         ] 30% 20.414s[==================>                                         ] 31% 20.774s[===================>                                        ] 31% 21.138s[===================>                                        ] 32% 21.713s[===================>                                        ] 33% 21.946s[====================>                                       ] 33% 22.324s[====================>                                       ] 34% 22.751s[====================>                                       ] 34% 23.063s[=====================>                                      ] 34% 23.304s[=====================>                                      ] 35% 23.646s[=====================>                                      ] 36% 24.007s[=====================>        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59.054s[=======================================================>    ] 92% 59.334s[=======================================================>    ] 92% 59.663s[=======================================================>    ] 93% 60.294s[========================================================>   ] 93% 60.583s[========================================================>   ] 94% 60.83s[=========================================================>  ] 94% 61.065s[=========================================================>  ] 95% 61.384s[=========================================================>  ] 95% 61.78s[=========================================================>  ] 96% 62.215s[==========================================================> ] 97% 62.583s[==========================================================> ] 97% 63.144s[==========================================================> ] 98% 63.457s[===========================================================>] 98% 63.901s[===========================================================>] 99% 64.227s[===========================================================>] 99% 64.551s\r\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid = np.linspace(-8, 8, 200)\n",
+    "dens_est_py = DensityEstimator(mixture_py)\n",
+    "densities_py = dens_est_py.estimate_density(grid)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## NNIG Hierarchy and DP Mixing in C++"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [],
+   "source": [
+    "mixing_cpp = DirichletProcessMixing(total_mass=5)\n",
+    "hierarchy_cpp = UnivariateNormal()\n",
+    "hierarchy_cpp.make_default_fixed_params(y_norm, 2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "mixture_cpp = MixtureModel(mixing_cpp, hierarchy_cpp)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [],
+   "source": [
+    "niter = 500\n",
+    "nburn = 100\n",
+    "algo = \"Neal2\""
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing... Done\n",
+      "Running Neal2 algorithm with NNIG hierarchies, DP mixing...\n",
+      "[============================================================] 100% 0.338s\r===========>                                               ] 20% 0.1s[============>                                               ] 20% 0.102s[============>                                               ] 20% 0.102s[============>                                               ] 21% 0.103s[============>                                               ] 21% 0.104s[=============>                                              ] 21% 0.105s[=============>                                              ] 22% 0.107s[=============>                                              ] 22% 0.108s[=============>                                              ] 23% 0.109s[==============>                                             ] 23% 0.11s[==============>                                             ] 23% 0.113s[==============>                                             ] 24% 0.114s[==============>                              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0.337s[============================================================] 100% 0.338s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "mixture_cpp.run_mcmc(y_norm, algorithm=algo, niter=niter, nburn=nburn)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[======================>                                     ] 37% 0.014s"
+     ]
+    }
+   ],
+   "source": [
+    "# grid = np.linspace(-8, 8, 200)\n",
+    "dens_est_cpp = DensityEstimator(mixture_cpp)\n",
+    "densities_cpp = dens_est_cpp.estimate_density(grid)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Comparing the density estimations for Python and C++ implementations of the models"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1080x576 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(15,8))\n",
+    "\n",
+    "axes[0].set_ylim(0, 0.4)\n",
+    "axes[1].set_ylim(0, 0.4)\n",
+    "\n",
+    "axes[0].hist(y_norm, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
+    "axes[0].plot(np.linspace(-8, 8, 1000), 0.7*ss.norm.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.norm.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
+    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
+    "axes[1].hist(y_norm, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
+    "axes[1].plot(np.linspace(-8, 8, 1000), 0.7*ss.norm.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.norm.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
+    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
+    "\n",
+    "idxs = [int((niter - nburn) * 0.25), int((niter - nburn) * 0.75)]\n",
+    "colors = [\"#8B008B\", \"#20B2AA\"]\n",
+    "for i, idx in enumerate(idxs):\n",
+    "    axes[0].plot(grid, densities_py[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
+    "    axes[1].plot(grid, densities_cpp[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
+    "\n",
+    "axes[0].plot(grid, np.mean(densities_py, axis=0), lw=4, label=\"predictive density lapnig\", color=\"#3A5FCD\")\n",
+    "axes[1].plot(grid, np.mean(densities_cpp, axis=0), lw=4, label=\"predictive density nnig ngg\", color=\"#3A5FCD\")\n",
+    "\n",
+    "axes[0].legend()\n",
+    "axes[1].legend()\n",
+    "\n",
+    "axes[0].set_title(\"NNIG Hierarchy, DP Mixing in Python\")\n",
+    "axes[1].set_title(\"NNIG Hierarchy, DP Mixing in C++\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/examples/estimate_py_density.ipynb b/docs/examples/estimate_py_density.ipynb
new file mode 100644
index 00000000..d12605fb
--- /dev/null
+++ b/docs/examples/estimate_py_density.ipynb
@@ -0,0 +1,724 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Univariate Density Estimation using Models Implemented in Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as ss\n",
+    "import sys\n",
+    "sys.path.append(\"../../\")\n",
+    "from pybmix.core.mixing import PythonMixing\n",
+    "from pybmix.core.hierarchy import PythonHierarchy\n",
+    "from pybmix.core.mixture_model import MixtureModel\n",
+    "from pybmix.estimators.density_estimator import DensityEstimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(2022)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Generate data as mixture of two Normals\n",
+    "\n",
+    "$$\n",
+    "y_i \\sim \\frac{7}{10} \\mathcal N(-3, 1) + \\frac{3}{10} \\mathcal N(3, 1), \\quad i=1, \\ldots, 200\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def sample_from_mixture_norm(weights, means, sds, n_data):\n",
+    "    n_comp = len(weights)\n",
+    "    clus_alloc = np.random.choice(np.arange(n_comp), p=weights, size=n_data)\n",
+    "    return np.random.normal(loc=means[clus_alloc], scale=sds[clus_alloc])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_norm = sample_from_mixture_norm(\n",
+    "    np.array([0.7, 0.3]), np.array([-3, 3]), np.array([1, 1]), 200)\n",
+    "plt.hist(y_norm, bins=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## The statistical model\n",
+    "\n",
+    "We assume the following model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "\\begin{equation}\n",
+    "\\begin{aligned}\n",
+    "y_i | \\tilde{p} &\\sim f(\\cdot) = \\int_{R \\times R^+} \\mathcal{Laplace}(\\cdot | \\mu, \\lambda ) \\tilde{p}(d\\mu, d\\lambda) \\\\\n",
+    "\\tilde{p} &\\sim DP(\\alpha, G_0)\n",
+    "\\end{aligned}\n",
+    "\\end{equation}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Here, we assume that $\\alpha = 5$ and $G_0(d\\mu, d\\lambda) = \\mathcal N(d\\mu | \\mu_0, \\sigma_0^2) \\times IG(d\\sigma^2 | \\nu_0, \\psi_0)$, i.e., $G_0$ is a normal-inverse gamma distribution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "\"LapNIG_Hierarchy\" is implemented in a .py file and here it is passed as argument to the generic PythonHierarchy object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Below we compare the performances of Laplace-NIG and Normal-NIG models. Please refer to \"Univariate Density Estimation via Dirichlet Process Mixture\" for the mathematical description of the Normal-NIG model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mixing = PythonMixing(\"DP_mixing\")\n",
+    "hierarchy_lapnig = PythonHierarchy(\"LapNIG_Hierarchy\")\n",
+    "hierarchy_nnig_ngg = PythonHierarchy(\"NNIG_Hierarchy_NGG\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mixture_lapnig = MixtureModel(mixing, hierarchy_lapnig)\n",
+    "mixture_nnig_ngg = MixtureModel(mixing, hierarchy_nnig_ngg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "niter = 300\n",
+    "nburn = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using hierarchy implementation in LapNIG_Hierarchy.py\n",
+      "Using mixing implementation in: DP_mixing.py\n",
+      "Initializing... Done\n",
+      "Running Neal8 algorithm (m=3 aux. blocks) with PythonHier hierarchies, PythonMix mixing...\n",
+      "[============================================================] 100% 183.372s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "mixture_lapnig.run_mcmc(y_norm, algorithm=\"Neal8\", niter=niter, nburn=nburn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using hierarchy implementation in NNIG_Hierarchy_NGG.py\n",
+      "Initializing... Using mixing implementation in: DP_mixing.py\n",
+      "Done\n",
+      "Running Neal2 algorithm with PythonHier hierarchies, PythonMix mixing...\n",
+      "[============================================================] 100% 71.254s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "mixture_nnig_ngg.run_mcmc(y_norm, algorithm=\"Neal2\", niter=niter, nburn=nburn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[============================================================] 100% 67.406s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid = np.linspace(-8, 8, 200)\n",
+    "dens_est_lapnig = DensityEstimator(mixture_lapnig)\n",
+    "densities_lapnig = dens_est_lapnig.estimate_density(grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[============================================================] 100% 43.005s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "dens_est_nnig_ngg = DensityEstimator(mixture_nnig_ngg)\n",
+    "densities_nnig_ngg = dens_est_nnig_ngg.estimate_density(grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(15,8))\n",
+    "\n",
+    "axes[0].set_ylim(0, 0.4)\n",
+    "axes[1].set_ylim(0, 0.4)\n",
+    "\n",
+    "axes[0].hist(y_norm, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
+    "axes[0].plot(np.linspace(-8, 8, 1000), 0.7*ss.norm.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.norm.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
+    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
+    "axes[1].hist(y_norm, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
+    "axes[1].plot(np.linspace(-8, 8, 1000), 0.7*ss.norm.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.norm.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
+    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
+    "\n",
+    "idxs = [int((niter - nburn) * 0.25), int((niter - nburn) * 0.75)]\n",
+    "colors = [\"#8B008B\", \"#20B2AA\"]\n",
+    "for i, idx in enumerate(idxs):\n",
+    "    axes[0].plot(grid, densities_lapnig[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
+    "    axes[1].plot(grid, densities_nnig_ngg[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
+    "    \n",
+    "axes[0].plot(grid, np.mean(densities_lapnig, axis=0), lw=4, label=\"predictive density lapnig\", color=\"#3A5FCD\")\n",
+    "axes[1].plot(grid, np.mean(densities_nnig_ngg, axis=0), lw=4, label=\"predictive density nnig ngg\", color=\"#3A5FCD\")\n",
+    "\n",
+    "axes[0].legend()\n",
+    "axes[1].legend()\n",
+    "\n",
+    "axes[0].set_title(\"LapNIG density estimation on mixture of Normal data\")\n",
+    "axes[1].set_title(\"NNIG density estimation on mixture of Normal data\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Generate data as mixture of two Laplace\n",
+    "\n",
+    "\n",
+    "Let us now repeat the procedure with data sampled from a mixture of Laplace \n",
+    "$$\n",
+    "y_i \\sim \\frac{7}{10} \\mathcal Laplace(-3, 1) + \\frac{3}{10} \\mathcal Laplace(3, 1), \\quad i=1, \\ldots, 200\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def sample_from_mixture_lap(weights, means, sds, n_data):\n",
+    "    n_comp = len(weights)\n",
+    "    clus_alloc = np.random.choice(np.arange(n_comp), p=weights, size=n_data)\n",
+    "    return np.random.laplace(loc=means[clus_alloc], scale=sds[clus_alloc])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_lap = sample_from_mixture_lap(\n",
+    "    np.array([0.7, 0.3]), np.array([-3, 3]), np.array([1, 1]), 200)\n",
+    "plt.hist(y_lap, bins=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mixture_lapnig_2 = MixtureModel(mixing, hierarchy_lapnig)\n",
+    "mixture_nnig_ngg_2 = MixtureModel(mixing, hierarchy_nnig_ngg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "niter = 300\n",
+    "nburn = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing... Using hierarchy implementation in LapNIG_Hierarchy.py\n",
+      "Using mixing implementation in: DP_mixing.py\n",
+      "Done\n",
+      "Running Neal8 algorithm (m=3 aux. blocks) with PythonHier hierarchies, PythonMix mixing...\n",
+      "[============================================================] 100% 188.127s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "mixture_lapnig_2.run_mcmc(y_lap, algorithm=\"Neal8\", niter=niter, nburn=nburn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing... Using hierarchy implementation in NNIG_Hierarchy_NGG.py\n",
+      "Using mixing implementation in: DP_mixing.py\n",
+      "Done\n",
+      "Running Neal2 algorithm with PythonHier hierarchies, PythonMix mixing...\n",
+      "[============================================================] 100% 87.616s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "mixture_nnig_ngg_2.run_mcmc(y_lap, algorithm=\"Neal2\", niter=niter, nburn=nburn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[============================================================] 100% 71.46s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid = np.linspace(-8, 8, 200)\n",
+    "dens_est_lapnig_2 = DensityEstimator(mixture_lapnig_2)\n",
+    "densities_lapnig_2 = dens_est_lapnig_2.estimate_density(grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[============================================================] 100% 53.188s\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "dens_est_nnig_ngg_2 = DensityEstimator(mixture_nnig_ngg_2)\n",
+    "densities_nnig_ngg_2 = dens_est_nnig_ngg_2.estimate_density(grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1,2, figsize=(15,8))\n",
+    "\n",
+    "axes[0].set_ylim(0, 0.4)\n",
+    "axes[1].set_ylim(0, 0.4)\n",
+    "\n",
+    "axes[0].hist(y_lap, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
+    "axes[0].plot(np.linspace(-8, 8, 1000), 0.7*ss.laplace.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.laplace.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
+    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
+    "axes[1].hist(y_lap, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
+    "axes[1].plot(np.linspace(-8, 8, 1000), 0.7*ss.laplace.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.laplace.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
+    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
+    "\n",
+    "idxs = [int((niter - nburn) * 0.25), int((niter - nburn) * 0.75)]\n",
+    "colors = [\"#8B008B\", \"#20B2AA\"]\n",
+    "for i, idx in enumerate(idxs):\n",
+    "    axes[0].plot(grid, densities_lapnig_2[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
+    "    axes[1].plot(grid, densities_nnig_ngg_2[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
+    "    \n",
+    "axes[0].plot(grid, np.mean(densities_lapnig_2, axis=0), lw=4, label=\"predictive density lapnig\", color=\"#3A5FCD\")\n",
+    "axes[1].plot(grid, np.mean(densities_nnig_ngg_2, axis=0), lw=4, label=\"predictive density nnig ngg\", color=\"#3A5FCD\")\n",
+    "\n",
+    "axes[0].legend()\n",
+    "axes[1].legend()\n",
+    "\n",
+    "axes[0].set_title(\"LapNIG density estimation on mixture of Laplace data\")\n",
+    "axes[1].set_title(\"NNIG density estimation on mixture of Laplace data\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/estimate_pyhier_density.ipynb b/docs/examples/estimate_pyhier_density.ipynb
deleted file mode 100644
index 51bd3aa2..00000000
--- a/docs/examples/estimate_pyhier_density.ipynb
+++ /dev/null
@@ -1,651 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# Univariate Density Estimation using Models Implemented in Python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import scipy.stats as ss\n",
-    "import sys\n",
-    "sys.path.append(\"../../\")\n",
-    "from pybmix.core.mixing import DirichletProcessMixing\n",
-    "from pybmix.core.hierarchy import PythonHierarchy\n",
-    "from pybmix.core.mixture_model import MixtureModel\n",
-    "from pybmix.estimators.density_estimator import DensityEstimator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "np.random.seed(2022)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Generate data as mixture of two Normals\n",
-    "\n",
-    "$$\n",
-    "y_i \\sim \\frac{7}{10} \\mathcal N(-3, 1) + \\frac{3}{10} \\mathcal N(3, 1), \\quad i=1, \\ldots, 200\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def sample_from_mixture_norm(weights, means, sds, n_data):\n",
-    "    n_comp = len(weights)\n",
-    "    clus_alloc = np.random.choice(np.arange(n_comp), p=weights, size=n_data)\n",
-    "    return np.random.normal(loc=means[clus_alloc], scale=sds[clus_alloc])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAANKklEQVR4nO3df4ykhV3H8fdHwNQIphAWPPnhtQ01xdoeZoskRKWlNJQjhf7RRIx4iSRXGzCQ0JiTJlr/O7UF/9DUXIX0ErGGBAiktNrzgjZNKnUPDwq5VprmROjJLaKB/lMDfP1j5+q57N7M7czs7Hd5v5LNzDwzs/N9cnvve272eZ5JVSFJ6ufHZj2AJGltDLgkNWXAJakpAy5JTRlwSWrq1PV8sbPPPru2bt26ni8pSe0dOHDgxaqaW758XQO+detWFhYW1vMlJam9JP+20nLfQpGkpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6Sm1vVITK2frbseWfNzD+/ePsFJJE2LW+CS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJampowJO8Jck3kzyR5OkkfzhYflaSfUmeGVyeOf1xJUnHjLIF/kPgA1X1XmAbcHWSy4BdwP6qugjYP7gtSVonQwNeS34wuHna4KuA64C9g+V7geunMaAkaWUjvQee5JQkB4GjwL6qegw4t6qOAAwuz5nalJKkNxgp4FX1WlVtA84HLk3y7lFfIMnOJAtJFhYXF9c4piRpuZPaC6Wq/hv4B+Bq4IUkWwAGl0dXec6eqpqvqvm5ubnxppUk/cgoe6HMJXnr4PpPAB8Evg08DOwYPGwH8NCUZpQkrWCUT6XfAuxNcgpLwb+vqr6U5BvAfUluAp4FPjbFOSVJywwNeFU9CVyywvL/BK6cxlCSpOE8ElOSmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJampowJNckOTRJIeSPJ3k1sHyTyd5PsnBwdc10x9XknTMqSM85lXg9qp6PMkZwIEk+wb33VVVn5neeJKk1QwNeFUdAY4Mrr+S5BBw3rQHkySd2Em9B55kK3AJ8Nhg0S1JnkxyT5IzV3nOziQLSRYWFxfHm1aS9CMjBzzJ6cD9wG1V9TLwOeAdwDaWttA/u9LzqmpPVc1X1fzc3Nz4E0uSgBEDnuQ0luJ9b1U9AFBVL1TVa1X1OvB54NLpjSlJWm6UvVAC3A0cqqo7j1u+5biHfRR4avLjSZJWM8peKJcDNwLfSnJwsOwO4IYk24ACDgMfn8J8kqRVjLIXyteBrHDXlyc/jiRpVB6JKUlNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1NQop5PVjGzd9cisR5C0gbkFLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpqaEBT3JBkkeTHErydJJbB8vPSrIvyTODyzOnP64k6ZhRtsBfBW6vqncBlwE3J7kY2AXsr6qLgP2D25KkdTI04FV1pKoeH1x/BTgEnAdcB+wdPGwvcP2UZpQkreCk3gNPshW4BHgMOLeqjsBS5IFzVnnOziQLSRYWFxfHHFeSdMzIAU9yOnA/cFtVvTzq86pqT1XNV9X83NzcWmaUJK1gpIAnOY2leN9bVQ8MFr+QZMvg/i3A0emMKElaySh7oQS4GzhUVXced9fDwI7B9R3AQ5MfT5K0mlHOB345cCPwrSQHB8vuAHYD9yW5CXgW+NhUJpQkrWhowKvq60BWufvKyY4jSRqVR2JKUlN+pJreYNyPcju8e/uEJtFG5c/IxuAWuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNeUn8kzZuJ9cIkmrcQtckpoy4JLUlAGXpKaGBjzJPUmOJnnquGWfTvJ8koODr2umO6YkablRtsC/AFy9wvK7qmrb4OvLkx1LkjTM0IBX1deAl9ZhFknSSRhnN8JbkvwmsADcXlX/tdKDkuwEdgJceOGFY7ycuhhn18nDu7dPcBJpc1vrLzE/B7wD2AYcAT672gOrak9VzVfV/Nzc3BpfTpK03JoCXlUvVNVrVfU68Hng0smOJUkaZk0BT7LluJsfBZ5a7bGSpOkY+h54ki8CVwBnJ3kO+APgiiTbgAIOAx+f3oiSpJUMDXhV3bDC4runMIsk6SR4MitJ6849lSbDQ+klqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqynOhjGCc8zZI0rS4BS5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNDQ14knuSHE3y1HHLzkqyL8kzg8szpzumJGm5UbbAvwBcvWzZLmB/VV0E7B/cliSto6EBr6qvAS8tW3wdsHdwfS9w/WTHkiQNs9b3wM+tqiMAg8tzVntgkp1JFpIsLC4urvHlJEnLTf2XmFW1p6rmq2p+bm5u2i8nSW8aaw34C0m2AAwuj05uJEnSKNYa8IeBHYPrO4CHJjOOJGlUo+xG+EXgG8DPJXkuyU3AbuCqJM8AVw1uS5LW0dBP5KmqG1a568oJzyJJOgl+pJr0JuVHBfbnofSS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrK3QilxtwV8M3NLXBJasqAS1JTBlySmjLgktSUAZekpt4Ue6H4m/o+xvmzOrx7+wQn0Ublz8j/cQtckpoy4JLUlAGXpKYMuCQ1ZcAlqak3xV4o0kbmXlJaK7fAJakpAy5JTY31FkqSw8ArwGvAq1U1P4mhJEnDTeI98PdX1YsT+D6SpJPgWyiS1NS4AS/gq0kOJNm50gOS7EyykGRhcXFxzJeTJB0zbsAvr6pfBD4M3JzkV5Y/oKr2VNV8Vc3Pzc2N+XKSpGPGCnhVfX9weRR4ELh0EkNJkoZbc8CT/GSSM45dBz4EPDWpwSRJJzbOXijnAg8mOfZ9/rqq/nYiU0mShlpzwKvqe8B7JziLJOkkuBuhJDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSU5P4UGNJamHrrkfW/NzDu7dPcJLJcAtckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNparW7cXm5+drYWFhTc8dZ/cfaZhxdxHz51PDjPMzluRAVc0vX+4WuCQ1ZcAlqamxAp7k6iTfSfLdJLsmNZQkabg1BzzJKcCfAx8GLgZuSHLxpAaTJJ3YOFvglwLfrarvVdX/AH8DXDeZsSRJw4xzMqvzgH8/7vZzwC8tf1CSncDOwc0fJPnOKt/vbODFMebZ6Dbz+rVft/zRqne1X7chNvP6bah1O8HP2Ch+dqWF4wQ8Kyx7wz6JVbUH2DP0myULK+0ms1ls5vVz3frazOu3mdftmHHeQnkOuOC42+cD3x9vHEnSqMYJ+D8DFyV5W5IfB34NeHgyY0mShlnzWyhV9WqSW4C/A04B7qmqp8eYZejbLM1t5vVz3frazOu3mdcNWOdD6SVJk+ORmJLUlAGXpKY2XMCT/M7g8Pynk/zxrOeZtCSfTFJJzp71LJOU5E+SfDvJk0keTPLWWc80rs16qogkFyR5NMmhwd+zW2c906QlOSXJvyT50qxnmaYNFfAk72fpaM73VNXPA5+Z8UgTleQC4Crg2VnPMgX7gHdX1XuAfwV+b8bzjGWTnyriVeD2qnoXcBlw8yZat2NuBQ7Neohp21ABBz4B7K6qHwJU1dEZzzNpdwG/ywoHPHVXVV+tqlcHN/+JpeMCOtu0p4qoqiNV9fjg+isshe682U41OUnOB7YDfznrWaZtowX8ncAvJ3ksyT8med+sB5qUJB8Bnq+qJ2Y9yzr4LeArsx5iTCudKmLTRO6YJFuBS4DHZjzKJP0pSxtKr894jqkb51D6NUny98BPr3DXp1ia50yW/lv3PuC+JG+vJvs6Dlm3O4APre9Ek3Wi9auqhwaP+RRL/0W/dz1nm4KRThXRWZLTgfuB26rq5VnPMwlJrgWOVtWBJFfMeJypW/eAV9UHV7svySeABwbB/maS11k6Ic3ies03jtXWLckvAG8DnkgCS28vPJ7k0qr6j3UccSwn+rMDSLIDuBa4sss/uiewqU8VkeQ0luJ9b1U9MOt5Juhy4CNJrgHeAvxUkr+qqt+Y8VxTsaEO5Eny28DPVNXvJ3knsB+4cBPE4P9JchiYr6oNc6a0cSW5GrgT+NWqavEP7okkOZWlX8ZeCTzP0qkjfn3Mo403hCxtRewFXqqq22Y8ztQMtsA/WVXXzniUqdlo74HfA7w9yVMs/dJox2aL9yb2Z8AZwL4kB5P8xawHGsfgF7LHThVxCLhvM8R74HLgRuADgz+rg4MtVjWzobbAJUmj22hb4JKkERlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ19b8KQBxTnhDZVgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_norm = sample_from_mixture_norm(\n",
-    "    np.array([0.7, 0.3]), np.array([-3, 3]), np.array([1, 1]), 200)\n",
-    "plt.hist(y_norm, bins=20)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## The statistical model\n",
-    "\n",
-    "We assume the following model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "\\begin{equation}\n",
-    "\\begin{aligned}\n",
-    "y_i | \\tilde{p} &\\sim f(\\cdot) = \\int_{R \\times R^+} \\mathcal{Laplace}(\\cdot | \\mu, \\lambda ) \\tilde{p}(d\\mu, d\\lambda) \\\\\n",
-    "\\tilde{p} &\\sim DP(\\alpha, G_0)\n",
-    "\\end{aligned}\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Here, we assume that $\\alpha = 5$ and $G_0(d\\mu, d\\lambda) = \\mathcal N(d\\mu | \\mu_0, \\sigma_0^2) \\times IG(d\\sigma^2 | \\nu_0, \\psi_0)$, i.e., $G_0$ is a normal-inverse gamma distribution."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "\"LapNIG_Hierarchy\" is implemented in a .py file and here it is passed as argument to the generic PythonHierarchy object."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Below we compare the performances of Laplace-NIG and Normal-NIG models. Please refer to \"Univariate Density Estimation via Dirichlet Process Mixture\" for the mathematical description of the Normal-NIG model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "mixing = DirichletProcessMixing(total_mass=5)\n",
-    "hierarchy_lapnig = PythonHierarchy(\"LapNIG_Hierarchy\")\n",
-    "hierarchy_nnig_ngg = PythonHierarchy(\"NNIG_Hierarchy_NGG\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "mixture_lapnig = MixtureModel(mixing, hierarchy_lapnig)\n",
-    "mixture_nnig_ngg = MixtureModel(mixing, hierarchy_nnig_ngg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "niter = 300\n",
-    "nburn = 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using hierarchy implementation in LapNIG_Hierarchy.py\n",
-      "Initializing... Done\n",
-      "Running Neal8 algorithm (m=3 aux. blocks) with PythonHier hierarchies, DP mixing...\n",
-      "[============================================================] 100% 127.453s\n",
-      "Done\n"
-     ]
-    }
-   ],
-   "source": [
-    "mixture_lapnig.run_mcmc(y_norm, algorithm=\"Neal8\", niter=niter, nburn=nburn)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using hierarchy implementation in NNIG_Hierarchy_NGG.py\n",
-      "Initializing... Done\n",
-      "Running Neal2 algorithm with PythonHier hierarchies, DP mixing...\n",
-      "[============================================================] 100% 30.028s\n",
-      "Done\n"
-     ]
-    }
-   ],
-   "source": [
-    "mixture_nnig_ngg.run_mcmc(y_norm, algorithm=\"Neal2\", niter=niter, nburn=nburn)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[============================================================] 100% 32.873s\n",
-      "Done\n"
-     ]
-    }
-   ],
-   "source": [
-    "grid = np.linspace(-8, 8, 200)\n",
-    "dens_est_lapnig = DensityEstimator(mixture_lapnig)\n",
-    "densities_lapnig = dens_est_lapnig.estimate_density(grid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[============================================================] 100% 15.978s\n",
-      "Done\n"
-     ]
-    }
-   ],
-   "source": [
-    "dens_est_nnig_ngg = DensityEstimator(mixture_nnig_ngg)\n",
-    "densities_nnig_ngg = dens_est_nnig_ngg.estimate_density(grid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1,2, figsize=(15,8))\n",
-    "\n",
-    "axes[0].set_ylim(0, 0.4)\n",
-    "axes[1].set_ylim(0, 0.4)\n",
-    "\n",
-    "axes[0].hist(y_norm, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
-    "axes[0].plot(np.linspace(-8, 8, 1000), 0.7*ss.norm.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.norm.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
-    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
-    "axes[1].hist(y_norm, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
-    "axes[1].plot(np.linspace(-8, 8, 1000), 0.7*ss.norm.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.norm.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
-    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
-    "\n",
-    "idxs = [int((niter - nburn) * 0.25), int((niter - nburn) * 0.75)]\n",
-    "colors = [\"#8B008B\", \"#20B2AA\"]\n",
-    "for i, idx in enumerate(idxs):\n",
-    "    axes[0].plot(grid, densities_lapnig[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
-    "    axes[1].plot(grid, densities_nnig_ngg[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
-    "    \n",
-    "axes[0].plot(grid, np.mean(densities_lapnig, axis=0), lw=4, label=\"predictive density lapnig\", color=\"#3A5FCD\")\n",
-    "axes[1].plot(grid, np.mean(densities_nnig_ngg, axis=0), lw=4, label=\"predictive density nnig ngg\", color=\"#3A5FCD\")\n",
-    "\n",
-    "axes[0].legend()\n",
-    "axes[1].legend()\n",
-    "\n",
-    "axes[0].set_title(\"LapNIG density estimation on mixture of Normal data\")\n",
-    "axes[1].set_title(\"NNIG density estimation on mixture of Normal data\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate data as mixture of two Laplace\n",
-    "\n",
-    "\n",
-    "Let us now repeat the procedure with data sampled from a mixture of Laplace \n",
-    "$$\n",
-    "y_i \\sim \\frac{7}{10} \\mathcal Laplace(-3, 1) + \\frac{3}{10} \\mathcal Laplace(3, 1), \\quad i=1, \\ldots, 200\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(2022)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sample_from_mixture_lap(weights, means, sds, n_data):\n",
-    "    n_comp = len(weights)\n",
-    "    clus_alloc = np.random.choice(np.arange(n_comp), p=weights, size=n_data)\n",
-    "    return np.random.laplace(loc=means[clus_alloc], scale=sds[clus_alloc])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_lap = sample_from_mixture_lap(\n",
-    "    np.array([0.7, 0.3]), np.array([-3, 3]), np.array([1, 1]), 200)\n",
-    "plt.hist(y_lap, bins=20)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mixture_lapnig_2 = MixtureModel(mixing, hierarchy_lapnig)\n",
-    "mixture_nnig_ngg_2 = MixtureModel(mixing, hierarchy_nnig_ngg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "niter = 300\n",
-    "nburn = 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing... Using hierarchy implementation in LapNIG_Hierarchy.py\n",
-      "Done\n",
-      "Running Neal8 algorithm (m=3 aux. blocks) with PythonHier hierarchies, DP mixing...\n",
-      "[============================================================] 100% 134.247s\n",
-      "Done\n"
-     ]
-    }
-   ],
-   "source": [
-    "mixture_lapnig_2.run_mcmc(y_lap, algorithm=\"Neal8\", niter=niter, nburn=nburn)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing... Using hierarchy implementation in NNIG_Hierarchy_NGG.py\n",
-      "Done\n",
-      "Running Neal2 algorithm with PythonHier hierarchies, DP mixing...\n",
-      "[============================================================] 100% 38.153s\n",
-      "Done\n"
-     ]
-    }
-   ],
-   "source": [
-    "mixture_nnig_ngg_2.run_mcmc(y_lap, algorithm=\"Neal2\", niter=niter, nburn=nburn)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[============================================================] 100% 38.614s\n",
-      "Done\n"
-     ]
-    }
-   ],
-   "source": [
-    "grid = np.linspace(-8, 8, 200)\n",
-    "dens_est_lapnig_2 = DensityEstimator(mixture_lapnig_2)\n",
-    "densities_lapnig_2 = dens_est_lapnig_2.estimate_density(grid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[============================================================] 100% 21.263s\n",
-      "Done\n"
-     ]
-    }
-   ],
-   "source": [
-    "dens_est_nnig_ngg_2 = DensityEstimator(mixture_nnig_ngg_2)\n",
-    "densities_nnig_ngg_2 = dens_est_nnig_ngg_2.estimate_density(grid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1,2, figsize=(15,8))\n",
-    "\n",
-    "axes[0].set_ylim(0, 0.4)\n",
-    "axes[1].set_ylim(0, 0.4)\n",
-    "\n",
-    "axes[0].hist(y_lap, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
-    "axes[0].plot(np.linspace(-8, 8, 1000), 0.7*ss.laplace.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.laplace.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
-    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
-    "axes[1].hist(y_lap, density=True, bins=50, alpha=0.3, color=\"#3A5FCD\")\n",
-    "axes[1].plot(np.linspace(-8, 8, 1000), 0.7*ss.laplace.pdf(np.linspace(-8, 8, 1000), -3, 1) + 0.3*ss.laplace.pdf(np.linspace(-8, 8, 1000), 3, 1),\n",
-    "             \"--\", lw=1, color=\"#3A5FCD\", label=\"true density\")\n",
-    "\n",
-    "idxs = [int((niter - nburn) * 0.25), int((niter - nburn) * 0.75)]\n",
-    "colors = [\"#8B008B\", \"#20B2AA\"]\n",
-    "for i, idx in enumerate(idxs):\n",
-    "    axes[0].plot(grid, densities_lapnig_2[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
-    "    axes[1].plot(grid, densities_nnig_ngg_2[idx, :], \"--\", label=\"iteration: {0}\".format(idx), color = colors[i])\n",
-    "    \n",
-    "axes[0].plot(grid, np.mean(densities_lapnig_2, axis=0), lw=4, label=\"predictive density lapnig\", color=\"#3A5FCD\")\n",
-    "axes[1].plot(grid, np.mean(densities_nnig_ngg_2, axis=0), lw=4, label=\"predictive density nnig ngg\", color=\"#3A5FCD\")\n",
-    "\n",
-    "axes[0].legend()\n",
-    "axes[1].legend()\n",
-    "\n",
-    "axes[0].set_title(\"LapNIG density estimation on mixture of Laplace data\")\n",
-    "axes[1].set_title(\"NNIG density estimation on mixture of Laplace data\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/examples/estimate_univ_density.ipynb b/docs/examples/estimate_univ_density.ipynb
index 7ca6694f..f4172388 100644
--- a/docs/examples/estimate_univ_density.ipynb
+++ b/docs/examples/estimate_univ_density.ipynb
@@ -260,4 +260,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/examples/test_run.py b/docs/examples/test_run.py
index 7cf9a984..0d73b44e 100644
--- a/docs/examples/test_run.py
+++ b/docs/examples/test_run.py
@@ -9,7 +9,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from pybmix.core.mixing import DirichletProcessMixing
+from pybmix.core.mixing import PythonMixing
 from pybmix.core.hierarchy import PythonHierarchy
 from pybmix.core.mixture_model import MixtureModel
 from pybmix.estimators.density_estimator import DensityEstimator
@@ -29,15 +29,15 @@ def sample_from_mixture(weights, means, sds, n_data):
 plt.hist(y, bins=20)
 plt.show()
 
-mixing = DirichletProcessMixing(total_mass=5)  # DP mixing
+mixing = PythonMixing(mix_implementation="DP_mixing")  # Python implementation of DP mixing
 
-hierarchy = PythonHierarchy("LapNIG_Hierarchy")
+hierarchy = PythonHierarchy("NNIG_Hierarchy_NGG")
 
 mixture = MixtureModel(mixing, hierarchy)
 
 niter = 110
 nburn = 10
-mixture.run_mcmc(y, algorithm="Neal8", niter=niter, nburn=nburn)
+mixture.run_mcmc(y, algorithm="Neal2", niter=niter, nburn=nburn)
 
 
 grid = np.linspace(-6, 6, 500)
diff --git a/pybmix/core/hierarchy.py b/pybmix/core/hierarchy.py
index d9e04ebc..53bf2c15 100644
--- a/pybmix/core/hierarchy.py
+++ b/pybmix/core/hierarchy.py
@@ -1,5 +1,5 @@
 import abc
-
+import logging
 import numpy as np
 
 import pybmix.proto.hierarchy_id_pb2 as hierarchy_id
@@ -10,7 +10,8 @@
 class BaseHierarchy(metaclass=abc.ABCMeta):
     @abc.abstractmethod
     def make_default_fixed_params(y):
-        pass
+        return logging.error("This function cannot be implemented!")
+
 
     def check_prior_params(self, self_prior_params, prior_params):
         if prior_params is None:
diff --git a/pybmix/core/mixing.py b/pybmix/core/mixing.py
index 8c716d86..aef6c395 100644
--- a/pybmix/core/mixing.py
+++ b/pybmix/core/mixing.py
@@ -6,7 +6,7 @@
 
 import pybmix.proto.mixing_id_pb2 as mixing_id
 from pybmix.proto.distribution_pb2 import BetaDistribution, GammaDistribution
-from pybmix.proto.mixing_prior_pb2 import DPPrior, PYPrior, TruncSBPrior
+from pybmix.proto.mixing_prior_pb2 import DPPrior, PYPrior, TruncSBPrior, PythonMixPrior
 from pybmix.utils.combinatorials import stirling, generalized_factorial_memoizer
 
 
@@ -20,7 +20,8 @@ def prior_cluster_distribution(self, grid, nsamples):
         Evaluates the prior probability of the number of clusters on a
         grid
         """
-        pass
+        return logging.error("This function cannot be implemented!")
+
 
 
 class DirichletProcessMixing(BaseMixing):
@@ -314,3 +315,18 @@ def _check_args(self, n_comp, strength=None, discount=None, beta_params=None):
 
         else:
             raise ValueError("Not enough parameters provided")
+
+
+class PythonMixing(BaseMixing):
+    ID = mixing_id.PythonMix
+    NAME = mixing_id.MixingId.Name(ID)
+
+    def __init__(self, mix_implementation):
+        self._build_prior_proto()
+        self.mix_implementation = mix_implementation
+
+    def prior_cluster_distribution(self, grid, nsamples):
+        pass
+
+    def _build_prior_proto(self):
+        self.prior_proto = PythonMixPrior()
diff --git a/pybmix/core/mixture_model.py b/pybmix/core/mixture_model.py
index 9fe5f1b8..d89ebf2f 100644
--- a/pybmix/core/mixture_model.py
+++ b/pybmix/core/mixture_model.py
@@ -43,7 +43,18 @@ def run_mcmc(self, y, algorithm="Neal2", niter=1000, nburn=500, rng_seed=-1):
 
         # If using PythonHierarchy as hier load the implementation from the corresponding file
         if self.hierarchy.NAME == 'PythonHier':
-            self._algo.load_py_hier_implementation(self.hierarchy.hier_implementation)
+            try:
+                self._algo.load_py_hier_implementation(self.hierarchy.hier_implementation)
+            except ModuleNotFoundError as e:
+                e.msg += "\nThe hier_implementation file {0} does not exist".format(self.hierarchy.hier_implementation)
+                raise
+
+        if self.mixing.NAME == 'PythonMix':
+            try:
+                self._algo.load_py_mix_implementation(self.mixing.mix_implementation)
+            except ModuleNotFoundError as e:
+                e.msg += "\nThe mix_implementation file {0} does not exist".format(self.mixing.mix_implementation)
+                raise
 
         with ostream_redirect(stdout=True, stderr=True):
             self._algo.run(y, niter, nburn, rng_seed)
diff --git a/pybmix/core/pybmixcpp/algorithm_wrapper.cpp b/pybmix/core/pybmixcpp/algorithm_wrapper.cpp
index 5a4e615f..7f12d2fe 100644
--- a/pybmix/core/pybmixcpp/algorithm_wrapper.cpp
+++ b/pybmix/core/pybmixcpp/algorithm_wrapper.cpp
@@ -48,6 +48,12 @@ void AlgorithmWrapper::load_py_hier_implementation(const std::string &module_nam
     }
 }
 
+void AlgorithmWrapper::load_py_mix_implementation(const std::string &module_name) {
+    if (dynamic_cast<PythonMixing *>(mixing.get()) != nullptr) {
+        static_cast<PythonMixing *>(mixing.get())->set_module(module_name.c_str());
+    }
+}
+
 void add_algorithm_wrapper(pybind11::module &m) {
     namespace py = pybind11;
     py::class_<AlgorithmWrapper>(m, "AlgorithmWrapper")
@@ -58,5 +64,6 @@ void add_algorithm_wrapper(pybind11::module &m) {
             .def("run", &AlgorithmWrapper::run)
             .def("eval_density", &AlgorithmWrapper::eval_density)
             .def("get_collector", &AlgorithmWrapper::get_collector)
-            .def("load_py_hier_implementation", &AlgorithmWrapper::load_py_hier_implementation);
+            .def("load_py_hier_implementation", &AlgorithmWrapper::load_py_hier_implementation)
+            .def("load_py_mix_implementation", &AlgorithmWrapper::load_py_mix_implementation);
 }
diff --git a/pybmix/core/pybmixcpp/algorithm_wrapper.hpp b/pybmix/core/pybmixcpp/algorithm_wrapper.hpp
index f0b27fdc..f2e72dea 100644
--- a/pybmix/core/pybmixcpp/algorithm_wrapper.hpp
+++ b/pybmix/core/pybmixcpp/algorithm_wrapper.hpp
@@ -48,6 +48,8 @@ class AlgorithmWrapper {
     const SerializedCollector &get_collector() const { return collector; }
 
     void load_py_hier_implementation(const std::string &module_name);
+
+    void load_py_mix_implementation(const std::string &module_name);
 };
 
 void add_algorithm_wrapper(pybind11::module &m);
diff --git a/pybmix/core/pybmixcpp/python_embedding/CMakeLists.txt b/pybmix/core/pybmixcpp/python_embedding/CMakeLists.txt
index 17a3b6ed..c5fffeec 100644
--- a/pybmix/core/pybmixcpp/python_embedding/CMakeLists.txt
+++ b/pybmix/core/pybmixcpp/python_embedding/CMakeLists.txt
@@ -1,9 +1,12 @@
 target_sources(bayesmix PUBLIC
         includes.h
         load_py_hierarchies.h
+        load_py_mixings.h
         auxiliary_functions.h
         auxiliary_functions.cc
         python_hierarchy.h
         python_hierarchy.cc
+        python_mixing.h
+        python_mixing.cc
         )
 
diff --git a/pybmix/core/pybmixcpp/python_embedding/includes.h b/pybmix/core/pybmixcpp/python_embedding/includes.h
index f2cccb65..fa8859df 100644
--- a/pybmix/core/pybmixcpp/python_embedding/includes.h
+++ b/pybmix/core/pybmixcpp/python_embedding/includes.h
@@ -2,5 +2,6 @@
 #define PYTHON_EMBEDDING_INCLUDES_H_
 
 #include "load_py_hierarchies.h"
+#include "load_py_mixings.h"
 
 #endif  // PYTHON_EMBEDDING_INCLUDES_H_
diff --git a/pybmix/core/pybmixcpp/python_embedding/load_py_mixings.h b/pybmix/core/pybmixcpp/python_embedding/load_py_mixings.h
new file mode 100644
index 00000000..280c837b
--- /dev/null
+++ b/pybmix/core/pybmixcpp/python_embedding/load_py_mixings.h
@@ -0,0 +1,29 @@
+#ifndef PYBMIX_LOAD_MIXINGS_2_H
+#define PYBMIX_LOAD_MIXINGS_2_H
+
+#include <functional>
+#include <memory>
+
+#include "bayesmix/src/mixings/abstract_mixing.h"
+#include "bayesmix/src/runtime/factory.h"
+#include "python_mixing.h"
+#include <iostream>
+
+//! Loads all available `Mixing` objects into the appropriate factory, so that
+//! they are ready to be chosen and used at runtime.
+
+template <class AbstractProduct>
+using Builder = std::function<std::shared_ptr<AbstractProduct>()>;
+
+using MixingFactory = Factory<bayesmix::MixingId, AbstractMixing>;
+
+__attribute__((constructor)) static void load_py_mixings() {
+    MixingFactory &factory = MixingFactory::Instance();
+    // Initialize factory builders
+    Builder<AbstractMixing> Pythonbuilder = []() {
+        return std::make_shared<PythonMixing>();
+    };
+    factory.add_builder(PythonMixing().get_id(), Pythonbuilder);
+}
+
+#endif //PYBMIX_LOAD_MIXINGS_2_H
diff --git a/pybmix/core/pybmixcpp/python_embedding/python_hierarchy.h b/pybmix/core/pybmixcpp/python_embedding/python_hierarchy.h
index f299814e..6fcded08 100644
--- a/pybmix/core/pybmixcpp/python_embedding/python_hierarchy.h
+++ b/pybmix/core/pybmixcpp/python_embedding/python_hierarchy.h
@@ -24,14 +24,6 @@
 namespace py = pybind11;
 using namespace py::literals;
 
-/*
-Python Hierarchy
-
-Deriving from AbstractHierarchy, the PythonHierarchy is a generic class for
-implementing models in Python. The methods marked with PYTHON in source are
-to be implemented in a .py file located in docs/examples. The state and hypers
-are generic, stored in std::vector containers.
-*/
 
 namespace PyHier {
 //! Custom container for State values
@@ -45,6 +37,14 @@ namespace PyHier {
     };
 }; // namespace Python
 
+//! Python Hierarchy
+//!
+//! Deriving from AbstractHierarchy, the PythonHierarchy is a generic class for
+//! implementing models in Python. This class allows to implement new hierarchies
+//! from a Python source file, while methods necessary for all hierarchies are
+//! implemented here. The methods marked with PYTHON in the source are to be implemented
+//! in a .py file. The state and hypers are generic, stored in std::vector containers.
+
 class PythonHierarchy : public AbstractHierarchy {
 public:
     PythonHierarchy() = default;
diff --git a/pybmix/core/pybmixcpp/python_embedding/python_mixing.cc b/pybmix/core/pybmixcpp/python_embedding/python_mixing.cc
new file mode 100644
index 00000000..c7253dc5
--- /dev/null
+++ b/pybmix/core/pybmixcpp/python_embedding/python_mixing.cc
@@ -0,0 +1,107 @@
+#include "python_mixing.h"
+
+#include <google/protobuf/stubs/casts.h>
+
+#include <memory>
+#include <stan/math/prim/prob.hpp>
+#include <vector>
+
+#include "mixing_prior.pb.h"
+#include "mixing_state.pb.h"
+#include "bayesmix/src/hierarchies/abstract_hierarchy.h"
+#include "bayesmix/src/utils/proto_utils.h"
+#include "bayesmix/src/utils/rng.h"
+
+#include <Eigen/Dense>
+#include <random>
+#include <sstream>
+#include <stan/math/prim/prob.hpp>
+#include <string>
+#include <vector>
+
+#include "algorithm_state.pb.h"
+#include "hierarchy_prior.pb.h"
+#include "ls_state.pb.h"
+
+#include <pybind11/stl.h>
+#include <pybind11/eigen.h>
+#include "auxiliary_functions.h"
+
+void PythonMixing::set_module(const std::string &module_name) {
+    std::cout << "Using mixing implementation in: " << module_name << ".py" << std::endl;
+
+    mix_implementation = py::module_::import(module_name.c_str());
+
+    is_conditional_evaluator = mix_implementation.attr("is_conditional");
+
+    update_state_evaluator = mix_implementation.attr("update_state");
+    initialize_state_evaluator = mix_implementation.attr("initialize_state");
+
+    if (is_conditional_evaluator().cast<bool>()) {
+        mixing_weights_evaluator = mix_implementation.attr("mixing_weights");
+    } else {
+        mass_existing_cluster_evaluator = mix_implementation.attr("mass_existing_cluster");
+        mass_new_cluster_evaluator = mix_implementation.attr("mass_new_cluster");
+    }
+}
+
+void PythonMixing::update_state(
+        const std::vector<std::shared_ptr<AbstractHierarchy>> &unique_values,
+        const std::vector<unsigned int> &allocations) {
+    auto priorcast = cast_prior();
+    unsigned int n = allocations.size();
+    synchronize_cpp_to_py_state(bayesmix::Rng::Instance().get(), py_gen);
+    py::list py_updated_state = update_state_evaluator(state.generic_state, bayesmix::to_eigen(priorcast->values()), n, unique_values.size());
+    state.generic_state = list_to_vector(py_updated_state);
+    synchronize_py_to_cpp_state(bayesmix::Rng::Instance().get(), py_gen);
+}
+
+double PythonMixing::mass_existing_cluster(
+        const unsigned int n, const unsigned int n_clust, const bool log,
+        const bool propto, const std::shared_ptr<AbstractHierarchy> hier) const {
+    double out = mass_existing_cluster_evaluator(n, n_clust, log, propto, hier->get_card(), state.generic_state).cast<double>();
+    return out;
+}
+
+double PythonMixing::mass_new_cluster(const unsigned int n,
+                                         const unsigned int n_clust,
+                                         const bool log,
+                                         const bool propto) const {
+    double out = mass_new_cluster_evaluator(n, n_clust, log, propto, state.generic_state).cast<double>();
+    return out;
+}
+
+void PythonMixing::set_state_from_proto(
+        const google::protobuf::Message &state_) {
+    auto &statecast = downcast_state(state_);
+    int size = statecast.general_state().size();
+    std::vector<double> aux_v{};
+    for (int i = 0; i < size; ++i) {
+        aux_v.push_back((statecast.general_state().data())[i]);
+    }
+    state.generic_state = aux_v;
+}
+
+std::shared_ptr<bayesmix::MixingState> PythonMixing::get_state_proto()
+const {
+    bayesmix::Vector state_;
+    state_.set_size(state.generic_state.size());
+    *state_.mutable_data() = {
+            state.generic_state.data(),
+            state.generic_state.data() + state.generic_state.size()};
+    auto out = std::make_shared<bayesmix::MixingState>();
+    out->mutable_general_state()->CopyFrom(state_);
+    return out;
+}
+
+//! PYTHON
+void PythonMixing::initialize_state() {
+    py::list state_py = initialize_state_evaluator();
+    state.generic_state = list_to_vector(state_py);
+}
+
+//! PYTHON
+Eigen::VectorXd PythonMixing::mixing_weights(const bool log, const bool propto) const {
+    py::list mixing_weights_py = mixing_weights_evaluator(log, propto, state.generic_state);
+    return mixing_weights_py.cast<Eigen::VectorXd>();
+}
\ No newline at end of file
diff --git a/pybmix/core/pybmixcpp/python_embedding/python_mixing.h b/pybmix/core/pybmixcpp/python_embedding/python_mixing.h
new file mode 100644
index 00000000..1f4ccbf7
--- /dev/null
+++ b/pybmix/core/pybmixcpp/python_embedding/python_mixing.h
@@ -0,0 +1,113 @@
+#ifndef PYBMIX_PYTHON_MIXING_H
+#define PYBMIX_PYTHON_MIXING_H
+
+#include <google/protobuf/message.h>
+#include <pybind11/embed.h>
+#include <pybind11/pybind11.h>
+
+#include <stan/math/rev.hpp>
+#include <Eigen/Dense>
+#include <memory>
+#include <vector>
+
+#include "bayesmix/src/mixings/base_mixing.h"
+#include "mixing_id.pb.h"
+#include "mixing_prior.pb.h"
+#include "bayesmix/src/hierarchies/abstract_hierarchy.h"
+#include "auxiliary_functions.h"
+
+namespace py = pybind11;
+using namespace py::literals;
+
+namespace PyMix {
+    struct State {
+        std::vector<double> generic_state;
+    };
+};  // namespace PyMix
+
+//! Python Mixing
+//!
+//! Deriving from BaseMixing, the PythonMixing is a generic class for
+//! implementing models in Python. This class allows to implement new mixings
+//! from a Python source file, while methods necessary for all mixings are
+//! implemented here. The methods marked with PYTHON in the source are to be implemented
+//! in a .py file. The state is generic, stored in a std::vector container.
+
+class PythonMixing
+        : public BaseMixing<PythonMixing, PyMix::State,bayesmix::PythonMixPrior> {
+public:
+    PythonMixing() = default;
+    ~PythonMixing() = default;
+
+    //! Sets the python module where the mixing is implemented
+    void set_module(const std::string &module_name);
+
+    //! Performs conditional update of state, given allocations and unique values
+    //! @param unique_values  A vector of (pointers to) Hierarchy objects
+    //! @param allocations    A vector of allocations label
+    void update_state(
+            const std::vector<std::shared_ptr<AbstractHierarchy>> &unique_values,
+            const std::vector<unsigned int> &allocations) override;
+
+    //! Read and set state values from a given Protobuf message
+    void set_state_from_proto(const google::protobuf::Message &state_) override;
+
+    //! Writes current state to a Protobuf message and return a shared_ptr
+    //! New hierarchies have to first modify the field 'oneof val' in the
+    //! MixingState message by adding the appropriate type
+    std::shared_ptr<bayesmix::MixingState> get_state_proto() const override;
+
+    //! Returns the Protobuf ID associated to this class
+    bayesmix::MixingId get_id() const override { return bayesmix::MixingId::PythonMix; }
+
+    //! Returns whether the mixing is conditional or marginal
+    bool is_conditional() const override { return is_conditional_evaluator().cast<bool>(); }
+
+protected:
+    //! Returns probability mass for an old cluster (for marginal mixings only)
+    //! @param n          Total dataset size
+    //! @param log        Whether to return logarithm-scale values or not
+    //! @param propto     Whether to include normalizing constants or not
+    //! @param hier       `Hierarchy` object representing the cluster
+    //! @return           Probability value
+    double mass_existing_cluster(
+            const unsigned int n, const unsigned int n_clust, const bool log,
+            const bool propto,
+            const std::shared_ptr<AbstractHierarchy> hier) const override;
+
+    //! Returns probability mass for a new cluster (for marginal mixings only)
+    //! @param n          Total dataset size
+    //! @param log        Whether to return logarithm-scale values or not
+    //! @param propto     Whether to include normalizing constants or not
+    //! @param n_clust    Current number of clusters
+    //! @return           Probability value
+    double mass_new_cluster(const unsigned int n, const unsigned int n_clust,
+                            const bool log, const bool propto) const override;
+
+    //! Returns mixing weights (for conditional mixings only)
+    //! @param log        Whether to return logarithm-scale values or not
+    //! @param propto     Whether to include normalizing constants or not
+    //! @return           The vector of mixing weights
+    Eigen::VectorXd mixing_weights(const bool log,
+                               const bool propto) const override;
+
+    //! Initializes state parameters to appropriate values
+    void initialize_state() override;
+
+    py::module_ numpy = py::module_::import("numpy");
+    py::module_ numpy_random = py::module_::import("numpy.random");
+    py::object py_engine = numpy_random.attr("MT19937")();
+    py::object py_gen = numpy_random.attr("Generator")(py_engine);
+
+    py::module_ mix_implementation;
+    py::object update_state_evaluator;
+    py::object mass_existing_cluster_evaluator;
+    py::object mass_new_cluster_evaluator;
+    py::object initialize_state_evaluator;
+    py::object is_conditional_evaluator;
+    py::object mixing_weights_evaluator;
+};
+
+
+
+#endif //PYBMIX_PYTHON_MIXING_H