Infinite hierarchical hidden Markov models by Heller, K, Teh, Y, Görür, D

“…In this paper we present the Infinite Hierarchical Hidden Markov Model (IHHMM), a nonparametric generalization of Hierarchical Hidden Markov Models (HHMMs). …”

The infinite factorial hidden Markov model by Van Gael, J, Teh, Y, Ghahramani, Z

“…We use this stochastic process to build a nonparametric extension of the factorial hidden Markov model. After constructing an inference scheme which combines slice sampling and dynamic programming we demonstrate how the infinite factorial hidden Markov model can be used for blind source separation.…”

Beam sampling for the infinite hidden Markov model by Van Gael, J, Saatci, Y, Teh, Y, Ghahramani, Z

“…The infinite hidden Markov model is a non-parametric extension of the widely used hidden Markov model. …”

Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks by Rao, V, Teh, Y

“…Our approach is based on the idea of uniformization, and sets up a Markov chain over paths by sampling a finite set of virtual jump times and then running a standard hidden Markov model forward filteringbackward sampling algorithm over states at the set of extant and virtual jump times. …”

Fast MCMC sampling for Markov jump processes and extensions by Rao, V, Teh, Y

“…The first step involves simulating a piecewise-constant inhomogeneous Poisson process, while for the second, we use a standard hidden Markov model forward filtering-backward sampling algorithm. …”

Modelling genetic variations with fragmentation-coagulation processes by Teh, Y, Blundell, C, Elliott, LT

“…As opposed to hidden Markov models, FCPs allow for flexible modelling of the number of clusters, and they avoid label switching non-identifiability problems. …”

A nonparametric HMM for genetic imputation and coalescent inference by Elliott, L, Teh, Y

“…Genetic sequence data are well described by hidden Markov models (HMMs) in which latent states correspond to clusters of similar mutation patterns. …”