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

Hirt, M; Dellaportas, P; (2019) Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers. In: Chaudhuri, K and Sugiyama, M, (eds.) Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research (PMLR): Okinawa, Japan. Green open access

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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.

Type: Proceedings paper
Title: Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
Event: 22nd International Conference on Artificial Intelligence and Statistics
Open access status: An open access version is available from UCL Discovery
Publisher version: http://proceedings.mlr.press/v89/
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10081209
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