## Bayesian Hidden Markov Models and Extensions

Abstract of the invited talk presented at CoNLL-2010 by

Zoubin Ghahramani

University of Cambridge & CMU

Hidden Markov models (HMMs) are one of the cornerstones of time-series
modelling. I will review HMMs, motivations for Bayesian approaches to
inference in them, and our work on variational Bayesian learning. I
will then focus on recent nonparametric extensions to HMMs.
Traditionally, HMMs have a known structure with a fixed number of
states and are trained using maximum likelihood techniques. The
infinite HMM (iHMM) allows a potentially unbounded number of hidden
states, letting the model use as many states as it needs for the
data. The recent development of 'Beam Sampling' --- an efficient
inference algorithm for iHMMs based on dynamic programming --- makes
it possible to apply iHMMs to large problems. I will show some
applications of iHMMs to unsupervised POS tagging and experiments with
parallel and distributed implementations. I will also describe a
factorial generalisation of the iHMM which makes it possible to have
an unbounded number of binary state variables, and can be thought of
as a time-series generalisation of the Indian buffet process. I will
conclude with thoughts on future directions in Bayesian modelling of
sequential data.

(pdf slides)

Last update: July 16, 2010.
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