Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers

Marcel Hirt, Petros Dellaportas
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:76-86, 2019.

Abstract

We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provides fully Bayesian inference of both the dynamic latent states and the static pa- rameters of the model. We build up on recent advances in computational statistics that combine variational methods with sequential Monte Carlo sampling and we demonstrate the advantages of performing full Bayesian inference over the static parameters rather than just performing variational EM approxima- tions. We illustrate how our approach enables scalable inference in multivariate stochastic volatility models and self-exciting point pro- cess models that allow for flexible dynamics in the latent intensity function.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-hirt19a, title = {Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers}, author = {Hirt, Marcel and Dellaportas, Petros}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {76--86}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/hirt19a/hirt19a.pdf}, url = {https://proceedings.mlr.press/v89/hirt19a.html}, abstract = {We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provides fully Bayesian inference of both the dynamic latent states and the static pa- rameters of the model. We build up on recent advances in computational statistics that combine variational methods with sequential Monte Carlo sampling and we demonstrate the advantages of performing full Bayesian inference over the static parameters rather than just performing variational EM approxima- tions. We illustrate how our approach enables scalable inference in multivariate stochastic volatility models and self-exciting point pro- cess models that allow for flexible dynamics in the latent intensity function.} }
Endnote
%0 Conference Paper %T Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers %A Marcel Hirt %A Petros Dellaportas %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-hirt19a %I PMLR %P 76--86 %U https://proceedings.mlr.press/v89/hirt19a.html %V 89 %X We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provides fully Bayesian inference of both the dynamic latent states and the static pa- rameters of the model. We build up on recent advances in computational statistics that combine variational methods with sequential Monte Carlo sampling and we demonstrate the advantages of performing full Bayesian inference over the static parameters rather than just performing variational EM approxima- tions. We illustrate how our approach enables scalable inference in multivariate stochastic volatility models and self-exciting point pro- cess models that allow for flexible dynamics in the latent intensity function.
APA
Hirt, M. & Dellaportas, P.. (2019). Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:76-86 Available from https://proceedings.mlr.press/v89/hirt19a.html.

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