Scan

AePPL can compute the log-density of measures defined as the output of an aesara.scan loop:

import aesara
import aesara.tensor as at
import numpy as np

srng = at.random.RandomStream()

p = np.array([0.9, 0.1])
S_0_rv = srng.categorical(p)

Gamma_at = at.matrix("Gamma")

def step_fn(S_tm1, Gamma):
   S_t = srng.categorical(Gamma[S_tm1])
   return S_t

 S_T_rv, _ = aesara.scan(
     fn=step_fn,
     outputs_info=[S_0_rv],
     sequences=[Gamm_at],
     n_steps=10,
 )

 logprob, (s_T_vv, s_0_vv) = aeppl.joint_logprob(S_T_rv, S_0_rv)

Rewrite

aeppl.scan.find_measurable_scans()

A NodeRewriter constructed from a function.

Log-density

aeppl.scan.logprob_ScanRV(op, values, *inputs, name=None, **kwargs)