Creating a Random Field distribution

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2

To answer my own question, in case someone has a similar problem: you can create a custom distribution as above and define a function sample_posterior.

class RF(Discrete):
    def __init__(self, cliques, **kwargs):
         # cliques is a list of list of indexes
        ...

    def sample_posterior(self, point=None):
        # Gibbs sample from P(A | B)
       ...
     
   
   def logp():
      # actually never need in my case
      return -np.inf

Then create a custom sampler:

class RandomFieldGibbs(ArrayStep):
   ...

   def step(point):
     # sample from P(A | B) 
     point['A'] = self.var.sample_posterior(point)
     return point

One can then sample as:

with model:
    step1 = pm.RandomFieldGibbs([A])
    step2 = pm.Metropolis([B])
trace = NDArray(model)
for i in range(draws):
   point = step1.step(point)
   point, states = step2.step(point)
   trace.record(point, state)
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