A Gibbs Sampler for Bayesian Nonparametric State-Space Models

Christos Merkatas, Simo Särkkä

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

3 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
KustantajaIEEE
Sivumäärä5
ISBN (elektroninen)979-8-3503-4485-1
DOI - pysyväislinkit
TilaJulkaistu - 18 maalisk. 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech and Signal Processing - Seoul, Korea, Republic of, Seoul, Etelä-Korea
Kesto: 14 huhtik. 202419 huhtik. 2024

Julkaisusarja

Nimi Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
LyhennettäICASSP
Maa/AlueEtelä-Korea
KaupunkiSeoul
Ajanjakso14/04/202419/04/2024

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