add example, fix minor issues

Author: aloctavodia

docs/source/examples.rst

@@ -12,6 +12,8 @@ Howto
    notebooks/sampler-stats.ipynb
    notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb
    notebooks/posterior_predictive.ipynb
+   notebooks/model_comparison.ipynb
+   notebooks/model_averaging.ipynb
    notebooks/howto_debugging.ipynb
    notebooks/PyMC3_tips_and_heuristic.ipynb
    notebooks/LKJ.ipynb

docs/source/notebooks/model_averaging.ipynb

@@ -0,0 +1,18 @@
+kcal.per.g,neocortex,log_mass
+0.490,0.552,0.668
+0.470,0.645,1.658
+0.560,0.645,1.681
+0.890,0.676,0.920
+0.920,0.688,-0.386
+0.800,0.589,-2.120
+0.460,0.617,-0.755
+0.710,0.603,-1.139
+0.680,0.700,0.438
+0.970,0.704,1.176
+0.840,0.734,2.510
+0.620,0.675,1.681
+0.540,0.713,3.569
+0.490,0.726,4.375
+0.480,0.702,3.707
+0.550,0.763,3.500
+0.710,0.755,4.006

…/source/notebooks/Model Comparison.ipynb …/source/notebooks/model_comparison.ipynbdocs/source/notebooks/Model Comparison.ipynb renamed to docs/source/notebooks/model_comparison.ipynb docs/source/notebooks/Model Comparison.ipynb renamed to docs/source/notebooks/model_comparison.ipynb

@@ -534,12 +534,20 @@ def sample_ppc(trace, samples=None, model=None, vars=None, size=None,
     if progressbar:
         indices = tqdm(indices, total=samples)
 
-    ppc = defaultdict(list)
-    for idx in indices:
-        param = trace[idx]
-        for var in vars:
-            ppc[var.name].append(var.distribution.random(point=param,
-                                                         size=size))
+    try:
+        ppc = defaultdict(list)
+        for idx in indices:
+            param = trace[idx]
+            for var in vars:
+                ppc[var.name].append(var.distribution.random(point=param,
+                                                             size=size))
+
+    except KeyboardInterrupt:
+        pass
+
+    finally:
+        if progressbar:
+            indices.close()
 
     return {k: np.asarray(v) for k, v in ppc.items()}
 
@@ -629,11 +637,20 @@ def sample_ppc_w(traces, samples=None, models=None, size=None, weights=None,
     if progressbar:
         indices = tqdm(indices, total=samples)
 
-    ppc = defaultdict(list)
-    for idx in indices:
-        param = trace[idx]
-        var = variables[idx]
-        ppc[var.name].append(var.distribution.random(point=param, size=size))
+    try:
+        ppc = defaultdict(list)
+        for idx in indices:
+            param = trace[idx]
+            var = variables[idx]
+            ppc[var.name].append(var.distribution.random(point=param,
+                                                         size=size))
+
+    except KeyboardInterrupt:
+        pass
+
+    finally:
+        if progressbar:
+            indices.close()
 
     return {k: np.asarray(v) for k, v in ppc.items()}