@@ -287,11 +287,6 @@ pz.maxent(dist, lower=0.1, upper=20, mass=0.95);
287287px.histogram(x=dist.rvs(1000), nbins=20, title="Gamma Distribution Samples")
288288```
289289
290- ``` {code-cell} ipython3
291- gamma_samples = pm.draw(pm.Gamma.dist(alpha=2, beta=0.2), 1000)
292- px.histogram(x=gamma_samples, nbins=20, title="Gamma Distribution Samples")
293- ```
294-
295290``` {code-cell} ipython3
296291t = df_country.select("days_since_100").to_numpy().flatten()
297292confirmed = df_country.select("confirmed").to_numpy().flatten()
@@ -338,7 +333,7 @@ fig.update_layout(
338333 yaxis=dict(range=[-100, 1000]),
339334 xaxis=dict(range=[0, 10]),
340335 template="plotly_white",
341- )
336+ );
342337```
343338
344339:::{admonition} Progress!
@@ -451,15 +446,15 @@ Before trusting our results, we must verify that the sampler has converged prope
451446:::
452447
453448``` {code-cell} ipython3
454- az.plot_trace(trace_exp3, var_names=["a", "b", "alpha"])
449+ az.plot_trace(trace_exp3, var_names=["a", "b", "alpha"]);
455450```
456451
457452``` {code-cell} ipython3
458453az.summary(trace_exp3, var_names=["a", "b", "alpha"])
459454```
460455
461456``` {code-cell} ipython3
462- az.plot_energy(trace_exp3)
457+ az.plot_energy(trace_exp3);
463458```
464459
465460:::{admonition} Convergence Checklist
@@ -558,7 +553,7 @@ for i, (name, trace) in enumerate(results.items()):
558553ax.set_xlabel("Growth rate (b)")
559554ax.set_ylabel("Density")
560555ax.set_title("Sensitivity to Prior Choice")
561- ax.legend()
556+ ax.legend();
562557```
563558
564559:::{admonition} Sensitivity Analysis Results
@@ -870,7 +865,7 @@ with logistic_model:
870865```
871866
872867``` {code-cell} ipython3
873- az.plot_trace(trace_logistic)
868+ az.plot_trace(trace_logistic);
874869```
875870
876871``` {code-cell} ipython3
@@ -967,7 +962,7 @@ with model_exp4:
967962with logistic_model:
968963 pm.compute_log_likelihood(trace_logistic)
969964
970- az.plot_compare(az.compare({"exp4": trace_exp4_full, "logistic": trace_logistic}))
965+ az.plot_compare(az.compare({"exp4": trace_exp4_full, "logistic": trace_logistic}));
971966```
972967
973968As you can see, the logistic model provides a much better fit to the data.
@@ -1031,7 +1026,7 @@ with logistic_model_us:
10311026```
10321027
10331028``` {code-cell} ipython3
1034- az.plot_trace(trace_logistic_us)
1029+ az.plot_trace(trace_logistic_us);
10351030```
10361031
10371032``` {code-cell} ipython3
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