- aeppl.dists.discrete_markov_chain(Gammas, gamma_0, size=None, srng=None, **kwargs)#
Construct a first-order discrete Markov chain distribution.
This characterizes vector random variables consisting of state indicator values (i.e.
M - 1) that are driven by a discrete Markov chain.
TensorVariable) – An array of transition probability matrices.
Gammastakes the shape
... x N x M x Mfor a state sequence of length
M-many distinct states. Each row,
r, in a transition probability matrix gives the probability of transitioning from state
rto each other state.
TensorVariable) – The initial state probabilities. The last dimension should be length
M, i.e. the number of distinct states.