Implementing a simple toy scenario with probabilistic reasoning in pymc

Questions version agnostic
5

Also, in your case it is perfectly possible to have everything end to end in PyMC, the trick is to pad the “missing” greeting:

weather_prob = np.asarray([.2, .8])  # Sunny, Not sunny
greeting_prob = np.asarray([[.6, .4, 0.],   # <= notice how the missing greeting has 0 probability
                            [.2, 0., .8]])

with pm.Model() as m:
    observed = pm.Data('observed', 0, mutable=True)
    weather = pm.Categorical('weather', weather_prob)
    greeting = pm.Categorical('greeting', 
                              aesara.shared(greeting_prob)[weather], 
                              observed=observed)
    # Simulate today’s greeting based on the (prior) probability of seeing sunny weather
    prior_sample = pm.sample_prior_predictive(1000)

# Infer today’s weather based on an observed greeting
traces = []
for obs in range(3):
    with m:
        observed.set_value(obs)
        traces.append(pm.sample(initvals={'weather': max(0, obs-1)}))

You can see that the result is the same as indexing to the prior_predictive @jessegrabowski explained:

_, axes = plt.subplots(1, 2, figsize=(5, 5), sharey=True)
for i, trace in enumerate(traces):
    x, y = np.unique(trace.posterior['weather'], return_counts=True)
    axes[0].bar(x + (i-1) / 5, y/y.sum(), width=.2);
    x_, y_ = np.unique(
        prior_sample.prior['weather'].values[prior_sample.prior_predictive['greeting'].values == i],
        return_counts=True)
    axes[1].bar(x_ + (i-1) / 5, y_/y_.sum(), width=.2);
axes[0].legend([f'observed = {i}' for i in range(3)])

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