2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSPTM-23.4
Paper Title Particle Gibbs Sampling for Regime-Switching State-Space Models
Authors Yousef El-Laham, Liu Yang, Heather Lynch, Petar Djuric, Monica Bugallo, Stony Brook University, United States
SessionSPTM-23: Bayesian Signal Processing
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Signal Processing Theory and Methods: [SSP] Statistical Signal Processing
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Abstract Regime-switching state-space models (RS-SSMs) are an important class of statistical models that can be used to represent real-world phenomena. Unlike regular state-space models, RS-SSMs allow for dynamic uncertainty in the state transition and observations distributions, making them much more expressive. Unfortunately, there are no existing Bayesian inference techniques for joint estimation of regimes, states, and model parameters in generic RS-SSMs. In this work, we develop a particle Gibbs sampling algorithm for Bayesian learning in RS-SSMs. We demonstrate the proposed inference approach on a synthetic data experiment related to an ecological application, where the goal is in estimating the abundance and demographic rates of penguins in the Antarctic.