The Markov Indian buffet process (Van Gael, Teh, and Ghahramani 2008) defines a distribution over an unbounded set of independent hidden Markov chains, where each chain is associated with a binary latent feature value that evolves over time according to Markov dynamics.
if we denote by M the total number of Markov chains associated with the latent feature values and by T the number of observations, mIBP defines a probability distribution over a binary matrix F with an unbounded number of rows M (M → ∞) and T columns.