add Bayesian workflow

Author: elizavetasemenova

04_probability_distributions.ipynb

@@ -1413,7 +1413,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Measuring distances btween distributions\n",
+        "## Measuring distances between distributions\n",
         "\n",
         "There are several ways to measure distances between two probability distributions with PDFs $p(x)$ and $q(x)$, each with its own characteristics and applications. \n",
         "\n",

05_Bayesian_inference.ipynb

@@ -244,9 +244,9 @@
         "\n",
         "What does it take?\n",
         "\n",
-        "- <font color='red'>`Data`</font>\n",
-        "- A generative model (how does the conditional <font color='teal'>`likelihood`</font> come about?)\n",
-        "- Our <font color='purple'>`beliefs`</font> before seeing the data.\n",
+        "- Data,\n",
+        "- A generative model,\n",
+        "- Our beliefs before seeing the data.\n",
         "\n",
         "What does it make?\n",
         "\n",

09_intro_to_Numpyro.ipynb

@@ -352,7 +352,7 @@
         "`````{admonition} Task: Point estimates for Bernoulli-beta coin flips\n",
         ":class: tip\n",
         "- You might have correctly noticed that we have not looked at the `Predictive` capability. Study the documentation of Numpyro (in particular, `numpyro.infer`) and demonstrate the `Predictive` command on the example shown above.\n",
-        "- Study the documentation of Numpyro (in particular, `numpyro.diagnostics`) to undertand what the `hpdi` command does. Apply it to the example shown above.\n",
+        "- Study the documentation of Numpyro (in particular, `numpyro.diagnostics`) to understand what the `hpdi` command does. Apply it to the example shown above.\n",
         "`````"
       ]
     },

11_Bayesian_workflow.ipynb

@@ -3,6 +3,7 @@
 ## Acknowledgements and links
 - AIMS and Ulrich personally for the invitation
 - [Machine Learning and Global Health](mlgh.net/people) network for many things, but in  particular for the (virtual, at the time) space where I learnt Numpyro through a reading group together with some MLGH members: Swapnil Mishra, Iwona Hawryluk, Tim Wolock, Theo Rashid, Giovanni Charles
+- [Deep Learning Indaba](https://deeplearningindaba.com/) for showing me how much ML enthisuams there is on the African continent and making me want to contribute 
 - Co-authors of the paper [Bayesian workflow for disease transmission modeling in Stan](https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9164) and all particiapnts of the regular Thursday Stan call which enabled me to co-author
 - Lorenzo Ciardo from Kellogg College at Oxford for telling me about the Buffon's needle problem
 - Richard McEarlth for posting the [prior-likelihood conflict example](https://twitter.com/rlmcelreath/status/1701165075493470644)

999_acknowledgements.md

@@ -13,4 +13,5 @@ chapters:
 - file: 08_PPLs.ipynb
 - file: 09_intro_to_Numpyro.ipynb
 - file: 10_focus_on_priors.ipynb
+- file: 11_Bayesian_workflow.ipynb
 - file: 999_acknowledgements.md