4 Specifically, the mIBP defines a distribution over an unbounded set of binary Markov chains, where each chain can be associated with a binary latent feature that evolves over time according to Markov dynamics.
if we denote by M the total number of feature chains and by T the number of observable components, the mIBP defines a probability distribution over a binary matrix F with T rows, which correspond to observations, and an unbounded number of columns M , which correspond to features.