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
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Paper Detail

Paper IDSPTM-23.6
Paper Title VARIATIONAL PARAMETER LEARNING IN SEQUENTIAL STATE-SPACE MODEL VIA PARTICLE FILTERING
Authors Chenhao Li, Simon Godsill, University of Cambridge, United Kingdom
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
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Parameter learning of the state-space model (SSM) plays a significant role in the modelling of time-series data and dynamical systems. However, the closed-form inference of the parameter posterior is often limited by sequential construction and non-linearity of the SSMs, which has led to the development of sampling-based algorithms such as particle Markov chain Monte Carlo (PMCMC). We present a novel algorithm, the particle filter variational inference (PF-VI) algorithm, which achieves closed-form learning of SSM parameters while tractably inferring the non-linear sequential states. We apply the algorithm to a popular non-linear SSM example and compare its performance against two competing PMCMC algorithms.