Switch-Reset Models : Exact and Approximate Inference

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Chris Bracegirdle, David Barber ;
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:190-198, 2011.

Abstract

Reset models are constrained switching latent Markov models in which the dynamics either continues according to a standard model, or the latent variable is resampled. We consider exact marginal inference in this class of models and their extension, the switch-reset models. A further convenient class of conjugate-exponential reset models is also discussed. For a length T time-series, exact filtering scales with T squared and smoothing T cubed. We discuss approximate filtering and smoothing routines that scale linearly with T. Applications are given to change-point models and reset linear dynamical systems. [pdf][supplementary]

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