The iFHMM framework uses the Markov Indian buffet process (mIBP) (Van Gael et al., 2008b) in order to represent each object as a sparse subset of a potentially unbounded set of latent features (Griffiths and Ghahramani, 2006;Ghahramani et al., 2007;Van Gael et al., 2008a).
4 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.