(How to) Sampling from a graphical model


I’m wondering if there is a tutorial or gist of code that demonstrates how to leverage pyro to sample from a defined graphical model. I.e.

Say you have the following graph with an associated distribution for each variable and function assigned to the edges, can pyro sample from this graphical model?

C ← A → B → D

If you write your model in such a way that in the end of function you return all the sample variables. Then just using model as function call will allow you to sample.

def Model():
    return A,B,C

this model can be used with all pyro functions. Calling Model() also samples variables for you.


def model(rain=None, sprinkler=None, grasswet=None):
    if rain is None:
        s_rain = pyro.sample('rain', dist.Bernoulli(0.2))
        s_rain = pyro.sample('rain', dist.Bernoulli(0.2), obs=rain)

    sprinkler_probs = ( 0.01 * s_rain) + (0.4 * (1 - s_rain))
    if sprinkler is None:
        s_sprinkler = pyro.sample('sprinkler', dist.Bernoulli(sprinkler_probs))
        s_sprinkler = pyro.sample('sprinkler', dist.Bernoulli(sprinkler_probs), obs=sprinkler)
    grasswet_probs = 0. * (1 - s_sprinkler) * (1 - s_rain) + 0.8 * (1 - s_sprinkler) * s_rain \
        + 0.9 * s_sprinkler * (1 - s_rain) + 0.99 * s_sprinkler * s_rain
    if grasswet is None:
        s_grasswet = pyro.sample('grasswet', dist.Bernoulli(grasswet_probs))
        s_grasswet = pyro.sample('grasswet', dist.Bernoulli(grasswet_probs), obs=grasswet)
    return s_rain, s_sprinkler, s_grasswet
# calling model as a function

returns triplet of samples.

One more way to sample is using predictive. In this method you don’t even need to return anything in your model def.

predictive = Predictive(model, guide=guide, num_samples=num_samples)
samples = {
    k: v.flatten().detach().numpy()
    for k, v in predictive(...).items()

Above model for this bayesian network.