The Infinite Hidden Markov Model (IHMM) extends hidden Markov models to have a countably infinite number of hidden states (Beal et al., 2002; Teh et al., 2006). We present a generalization of this framework that introduces nearly block-diagonal structure in the transitions between the hidden states, where blocks correspond to “subbehaviors” exhibited by data sequences. In identifying such struc...

We study sequential coding of Markov sources under an error propagation constraint. An encoder sequentially compresses a sequence of vector-sources that are spatially i.i.d. but temporally correlated according to a first-order Markov process. The channel erases up to B packets in a single burst, but reveals all other packets to the destination. The destination is required to reproduce all the s...

In this paper, knots are considered as smooth embeddings of a circle into ℝ3 defined by their flat diagrams. We propose new method coding T-graphs describing the torsion structure on diagram. For coding, we introduce conceptions cycle and block describe transformations under first third Reidemeister moves applied to diagram knot.