Implementing a custom graphical model

Hi there,

I am very new to pyro and want to implement a graphical model / HMM / Baysesian network that looks like this
and if possible later one which has an additional connection
where the orange color marks observed variables.

So far, I read the HMM tutorial, but the closest model there is the Dynamic Bayesian Network (model_4), which has 2 hidden states and 1 observation. In the case I want to implement, there is 1 discrete hidden state and 2 continious observable states.

Do I understand it correctly, that the number in to_event expresses causal ordering?
Can/Should I use parallel enumeration as discribed in this tutorial?

I am thankful for a point (e.g. code snippet) to start from.

Hi @famura, That looks like an interesting model, and very suitable for Pyro.

Do I understand it correctly, that the number in to_event expresses causal ordering?

No, rather .to_event() is a way to change how Pyro interpret’s the shape of a distribution. Pyro distributions (like PyTorch and Tensorflow distributions) split shape into batch_shape + event_shape, where things are iid over batch_shape but may have dependencies among event_shape. .to_event() tells pyro to treat sum number of batch dims as event dims even though they are iid. This is useful e.g. for producing a diagonal normal distribution.

Can/Should I use parallel enumeration …?

Yes you can use parallel enumeration. Your model should look something like

def model(s, a):
    assert len(s) == len(a)
    for t in pyro.markov(range(len(s))):
        if t == 0:  # initial step
            pyro.sample("s_0", dist.Normal(s_init_fn(), 1), obs=s[t])
            pyro.sample("a_0", dist.Normal(a_init_fn(), 1), obs=a[t])
            rho = pyro.sample("rho_0",
                        dist.Normal(s_step_fn(s[t-1], rho[t-1], a[t-1]), 1), obs=s[t])
                        dist.Normal(a_step_fn(rho[t-1]), 1), obs=a[t])
            rho = pyro.sample("rho_{}".format(t),
                              dist.Categorical(rho_step_fn(s[t], a[t])))

You should be able to train using TraceEnum_ELBO and should be able to sample rho from the posterior or MAP estimate rho using infer_discrete.

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Hi @fritzo, that you very much for your detailed answer. I will give it a try!

One final thing: from the fact that you did not mention the guide I infer that the auto guide will do just fine.

Hi @famura, because all of your latent variables are discrete and you are using enumeration, you can use an empty guide:

def guide(s, a):

In this case Stochastic Variational Inference becomes trivial, and you’ll really be doing deterministic first-order Baum-Welch learning. If you later want to make this a hierarchical model, you could include latent global continuous variables and use an AutoGuide to learn those variables.