A Gibbs Sampler for Bayesian Nonparametric State-Space Models

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A4 Artikkeli konferenssijulkaisussa
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Date

2024-03-18

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Department of Electrical Engineering and Automation

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Mcode

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Language

en

Pages

5

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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

Abstract

A common assumption in state space models is that the state and observation noise is Gaussian. However, there are cases where this assumption is violated and is chosen for computational convenience. In this article, we present a state space model whose noise processes are modeled via highly flexible density functions based on Bayesian nonparametric priors with decreasing weights. We are focusing on a system identification problem were the aim is to estimate the parameters and the states of the (possibly) nonlinear dynamical system along with its noise processes using Gibbs sampling. Experiments in simulated data show that the nonparametric model outperforms parametric models especially when the distributions of the noise processes depart from Gaussianity.

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Keywords

Bayesian nonparametrics, Gibbs sampling, State-space models

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DOI

10.1109/ICASSP48485.2024.10446518

Citation

Merkatas, C & Särkkä, S 2024, A Gibbs Sampler for Bayesian Nonparametric State-Space Models . in 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings . Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, pp. 13236-13240, IEEE International Conference on Acoustics, Speech and Signal Processing, Seoul, Korea, Republic of, 14/04/2024 . https://doi.org/10.1109/ICASSP48485.2024.10446518

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https://urn.fi/URN:NBN:fi:aalto-202406124250

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[article-cris] Sähkötekniikan korkeakoulu / ELEC