Merge pull request #24 from iamhaingo/main

Fix typos in 18_GP_inference.ipynb and 19_geostatistics.ipynb

18_GP_inference.ipynb

@@ -20,7 +20,7 @@
         "Here $\\{(x_i, y_i)\\}_{i=1}^n$ are pairs of observations $y_i$, and locations of those observations $x_i$. The role of $f(x)$ now is to serve as a <font color='orange'>latent field</font> capturing dependencies between locations $x$. The expression $\\Pi_i p(y_i \\vert f(x_i))$ provides a likelihood, allowing us to link observed data to the model and enabling parameter inference.\n",
         "\n",
         "```{margin}\n",
-        "The task of predicting at unobserved locations is often referred to as **kriging** and is the underlying concenpt in spatial statistics. We will talk about it in the next chapter.\n",
+        "The task of predicting at unobserved locations is often referred to as **kriging** and is the underlying concept in spatial statistics. We will talk about it in the next chapter.\n",
         "```\n",
         "The task that we usually want to solve is twofold: we want to infer parameters involed in the model, as well as make predictions at unobserved locations $x_*.$\n",
         "\n",

19_geostatistics.ipynb

@@ -198,7 +198,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The command above `GaussianProcessRegressor` worked out very well. However, if we want to work with non-Gaussian likelihoods, and more complex models overall, such striaghtforward tools won't be available to us. Hence, we need to understand now to implement such models in Numpyro."
+        "The command above `GaussianProcessRegressor` worked out very well. However, if we want to work with non-Gaussian likelihoods, and more complex models overall, such straightforward tools won't be available to us. Hence, we need to understand how to implement such models in Numpyro."
       ]
     },
     {