Spellcheck all notebooks (pymc-devs#492)

Author: Armavica

examples/case_studies/BART_introduction.ipynb

@@ -279,7 +279,7 @@
    "id": "8633b7b4",
    "metadata": {},
    "source": [
-    "The next figure shows 3 trees. As we can see these are very simple function and definitely not very good approximators by themselves. Inspecting individuals trees is generally not necessary when working with BART, we are showing them just so we can gain further intuition on the inner workins of BART."
+    "The next figure shows 3 trees. As we can see these are very simple function and definitely not very good approximators by themselves. Inspecting individuals trees is generally not necessary when working with BART, we are showing them just so we can gain further intuition on the inner workings of BART."
    ]
   },
   {

examples/case_studies/BART_introduction.myst.md

@@ -117,7 +117,7 @@ The following figure shows two samples of $\mu$ from the posterior.
 plt.step(x_data, idata_coal.posterior["μ"].sel(chain=0, draw=[3, 10]).T);
 ```
 
-The next figure shows 3 trees. As we can see these are very simple function and definitely not very good approximators by themselves. Inspecting individuals trees is generally not necessary when working with BART, we are showing them just so we can gain further intuition on the inner workins of BART.
+The next figure shows 3 trees. As we can see these are very simple function and definitely not very good approximators by themselves. Inspecting individuals trees is generally not necessary when working with BART, we are showing them just so we can gain further intuition on the inner workings of BART.
 
 ```{code-cell} ipython3
 bart_trees = μ_.owner.op.all_trees

examples/case_studies/binning.ipynb

@@ -758,7 +758,7 @@
    "id": "71c3cf64",
    "metadata": {},
    "source": [
-    "Pretty good! And we can access the posterior mean estimates (stored as [xarray](http://xarray.pydata.org/en/stable/index.html) types) as below. The MCMC samples arrive back in a 2D matrix with one dimension for the MCMC chain (`chain`), and one for the sample number (`draw`). We can calculate the overal posterior average with `.mean(dim=[\"draw\", \"chain\"])`."
+    "Pretty good! And we can access the posterior mean estimates (stored as [xarray](http://xarray.pydata.org/en/stable/index.html) types) as below. The MCMC samples arrive back in a 2D matrix with one dimension for the MCMC chain (`chain`), and one for the sample number (`draw`). We can calculate the overall posterior average with `.mean(dim=[\"draw\", \"chain\"])`."
    ]
   },
   {

examples/case_studies/binning.myst.md

@@ -299,7 +299,7 @@ Recall that we used `mu = -2` and `sigma = 2` to generate the data.
 az.plot_posterior(trace1, var_names=["mu", "sigma"], ref_val=[true_mu, true_sigma]);
 ```
 
-Pretty good! And we can access the posterior mean estimates (stored as [xarray](http://xarray.pydata.org/en/stable/index.html) types) as below. The MCMC samples arrive back in a 2D matrix with one dimension for the MCMC chain (`chain`), and one for the sample number (`draw`). We can calculate the overal posterior average with `.mean(dim=["draw", "chain"])`.
+Pretty good! And we can access the posterior mean estimates (stored as [xarray](http://xarray.pydata.org/en/stable/index.html) types) as below. The MCMC samples arrive back in a 2D matrix with one dimension for the MCMC chain (`chain`), and one for the sample number (`draw`). We can calculate the overall posterior average with `.mean(dim=["draw", "chain"])`.
 
 ```{code-cell} ipython3
 :tags: []

examples/case_studies/conditional-autoregressive-model.ipynb

@@ -286,11 +286,11 @@
     "        self.mode = 0.0\n",
     "\n",
     "    def get_mu(self, x):\n",
-    "        def weigth_mu(w, a):\n",
+    "        def weight_mu(w, a):\n",
     "            a1 = tt.cast(a, \"int32\")\n",
     "            return tt.sum(w * x[a1]) / tt.sum(w)\n",
     "\n",
-    "        mu_w, _ = scan(fn=weigth_mu, sequences=[self.w, self.a])\n",
+    "        mu_w, _ = scan(fn=weight_mu, sequences=[self.w, self.a])\n",
     "\n",
     "        return mu_w\n",
     "\n",

examples/case_studies/conditional-autoregressive-model.myst.md

@@ -220,11 +220,11 @@ class CAR(distribution.Continuous):
         self.mode = 0.0
 
     def get_mu(self, x):
-        def weigth_mu(w, a):
+        def weight_mu(w, a):
             a1 = tt.cast(a, "int32")
             return tt.sum(w * x[a1]) / tt.sum(w)
 
-        mu_w, _ = scan(fn=weigth_mu, sequences=[self.w, self.a])
+        mu_w, _ = scan(fn=weight_mu, sequences=[self.w, self.a])
 
         return mu_w
 

examples/case_studies/hierarchical_partial_pooling.ipynb

@@ -42,7 +42,7 @@
     "\n",
     "It may be possible to cluster groups of \"similar\" players, and estimate group averages, but using a hierarchical modeling approach is a natural way of sharing information that does not involve identifying *ad hoc* clusters.\n",
     "\n",
-    "The idea of hierarchical partial pooling is to model the global performance, and use that estimate to parameterize a population of players that accounts for differences among the players' performances. This tradeoff between global and individual performance will be automatically tuned by the model. Also, uncertainty due to different number of at bats for each player (*i.e.* informatino) will be automatically accounted for, by shrinking those estimates closer to the global mean.\n",
+    "The idea of hierarchical partial pooling is to model the global performance, and use that estimate to parameterize a population of players that accounts for differences among the players' performances. This tradeoff between global and individual performance will be automatically tuned by the model. Also, uncertainty due to different number of at bats for each player (*i.e.* information) will be automatically accounted for, by shrinking those estimates closer to the global mean.\n",
     "\n",
     "For far more in-depth discussion please refer to Stan [tutorial](http://mc-stan.org/documentation/case-studies/pool-binary-trials.html) {cite:p}`carpenter2016hierarchical` on the subject. The model and parameter values were taken from that example."
    ]

examples/case_studies/hierarchical_partial_pooling.myst.md

@@ -45,7 +45,7 @@ Of course, neither approach is realistic. Clearly, all players aren't equally sk
 
 It may be possible to cluster groups of "similar" players, and estimate group averages, but using a hierarchical modeling approach is a natural way of sharing information that does not involve identifying *ad hoc* clusters.
 
-The idea of hierarchical partial pooling is to model the global performance, and use that estimate to parameterize a population of players that accounts for differences among the players' performances. This tradeoff between global and individual performance will be automatically tuned by the model. Also, uncertainty due to different number of at bats for each player (*i.e.* informatino) will be automatically accounted for, by shrinking those estimates closer to the global mean.
+The idea of hierarchical partial pooling is to model the global performance, and use that estimate to parameterize a population of players that accounts for differences among the players' performances. This tradeoff between global and individual performance will be automatically tuned by the model. Also, uncertainty due to different number of at bats for each player (*i.e.* information) will be automatically accounted for, by shrinking those estimates closer to the global mean.
 
 For far more in-depth discussion please refer to Stan [tutorial](http://mc-stan.org/documentation/case-studies/pool-binary-trials.html) {cite:p}`carpenter2016hierarchical` on the subject. The model and parameter values were taken from that example.
 

examples/case_studies/item_response_nba.ipynb

@@ -248,7 +248,7 @@
      "output_type": "stream",
      "text": [
       "Number of observed plays: 46861\n",
-      "Number of disadvanteged players: 770\n",
+      "Number of disadvantaged players: 770\n",
       "Number of committing players: 789\n",
       "Global probability of a foul being called: 23.3%\n",
       "\n",
@@ -378,7 +378,7 @@
     "\n",
     "# Display of main dataframe with some statistics\n",
     "print(f\"Number of observed plays: {len(df)}\")\n",
-    "print(f\"Number of disadvanteged players: {len(disadvantaged)}\")\n",
+    "print(f\"Number of disadvantaged players: {len(disadvantaged)}\")\n",
     "print(f\"Number of committing players: {len(committing)}\")\n",
     "print(f\"Global probability of a foul being called: \" f\"{100*round(df.foul_called.mean(),3)}%\\n\\n\")\n",
     "df.head()"

examples/case_studies/item_response_nba.myst.md

@@ -140,7 +140,7 @@ df.index.name = "play_id"
 
 # Display of main dataframe with some statistics
 print(f"Number of observed plays: {len(df)}")
-print(f"Number of disadvanteged players: {len(disadvantaged)}")
+print(f"Number of disadvantaged players: {len(disadvantaged)}")
 print(f"Number of committing players: {len(committing)}")
 print(f"Global probability of a foul being called: " f"{100*round(df.foul_called.mean(),3)}%\n\n")
 df.head()