Switch-Reset Models : Exact and Approximate Inference

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 $T^2$ squared and smoothing $T^3$ 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-bracegirdle11a, title = {Switch-Reset Models : Exact and Approximate Inference}, author = {Bracegirdle, Chris and Barber, David}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {190--198}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/bracegirdle11a/bracegirdle11a.pdf}, url = {https://proceedings.mlr.press/v15/bracegirdle11a.html}, 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 $T^2$ squared and smoothing $T^3$ 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.} }
Endnote
%0 Conference Paper %T Switch-Reset Models : Exact and Approximate Inference %A Chris Bracegirdle %A David Barber %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-bracegirdle11a %I PMLR %P 190--198 %U https://proceedings.mlr.press/v15/bracegirdle11a.html %V 15 %X 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 $T^2$ squared and smoothing $T^3$ 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.
RIS
TY - CPAPER TI - Switch-Reset Models : Exact and Approximate Inference AU - Chris Bracegirdle AU - David Barber BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-bracegirdle11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 190 EP - 198 L1 - http://proceedings.mlr.press/v15/bracegirdle11a/bracegirdle11a.pdf UR - https://proceedings.mlr.press/v15/bracegirdle11a.html AB - 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 $T^2$ squared and smoothing $T^3$ 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. ER -
APA
Bracegirdle, C. & Barber, D.. (2011). Switch-Reset Models : Exact and Approximate Inference. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:190-198 Available from https://proceedings.mlr.press/v15/bracegirdle11a.html.

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