Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs

Roland Hostettler; Simo Särkkä

doi:10.1109/MLSP.2019.8918852

Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs

Uppsala University

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

Abstrakti

Particle Gibbs with ancestor sampling is an efficient and statistically principled algorithm for learning of dynamic systems. However, the ancestor sampling step used to improve mixing of the Markov chain requires the possibly expensive calculation of a set of ancestor weights for the complete particle system. In this paper, we propose a rejection-sampling-based algorithm for ancestor sampling in particle Gibbs that mitigates this problem. Additionally, performance guarantees and a fallback strategy to prevent suffering from high rejection rates are discussed. The performance of the method is illustrated in two numerical examples.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
Kustantaja	IEEE
Sivumäärä	6
ISBN (elektroninen)	9781728108247
DOI - pysyväislinkit	https://doi.org/10.1109/MLSP.2019.8918852
Tila	Julkaistu - 1 lokak. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Workshop on Machine Learning for Signal Processing - Pittsburgh, Yhdysvallat Kesto: 13 lokak. 2019 → 16 lokak. 2019 Konferenssinumero: 29

Julkaisusarja

Nimi	IEEE International Workshop on Machine Learning for Signal Processing
Kustantaja	IEEE
ISSN (painettu)	2161-0363
ISSN (elektroninen)	2161-0371

Workshop

Workshop	IEEE International Workshop on Machine Learning for Signal Processing
Lyhennettä	MLSP
Maa/Alue	Yhdysvallat
Kaupunki	Pittsburgh
Ajanjakso	13/10/2019 → 16/10/2019

Pääsy asiakirjaan

10.1109/MLSP.2019.8918852

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Link to publication in Scopus

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@inproceedings{a8cc8558bab34564995776cee1e4a510,

title = "Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs",

abstract = "Particle Gibbs with ancestor sampling is an efficient and statistically principled algorithm for learning of dynamic systems. However, the ancestor sampling step used to improve mixing of the Markov chain requires the possibly expensive calculation of a set of ancestor weights for the complete particle system. In this paper, we propose a rejection-sampling-based algorithm for ancestor sampling in particle Gibbs that mitigates this problem. Additionally, performance guarantees and a fallback strategy to prevent suffering from high rejection rates are discussed. The performance of the method is illustrated in two numerical examples.",

keywords = "Particle filters, Particle Markov chain Monte Carlo, Statistical learning",

author = "Roland Hostettler and Simo S{\"a}rkk{\"a}",

year = "2019",

month = oct,

day = "1",

doi = "10.1109/MLSP.2019.8918852",

language = "English",

series = "IEEE International Workshop on Machine Learning for Signal Processing",

publisher = "IEEE",

booktitle = "Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019",

address = "United States",

note = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP ; Conference date: 13-10-2019 Through 16-10-2019",

}

Hostettler, R & Särkkä, S 2019, Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs. julkaisussa Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019., 8918852, IEEE International Workshop on Machine Learning for Signal Processing, IEEE, IEEE International Workshop on Machine Learning for Signal Processing, Pittsburgh, Pennsylvania, Yhdysvallat, 13/10/2019. https://doi.org/10.1109/MLSP.2019.8918852

Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs. / Hostettler, Roland; Särkkä, Simo.
Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019. IEEE, 2019. 8918852 (IEEE International Workshop on Machine Learning for Signal Processing).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs

AU - Hostettler, Roland

AU - Särkkä, Simo

N1 - Conference code: 29

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Particle Gibbs with ancestor sampling is an efficient and statistically principled algorithm for learning of dynamic systems. However, the ancestor sampling step used to improve mixing of the Markov chain requires the possibly expensive calculation of a set of ancestor weights for the complete particle system. In this paper, we propose a rejection-sampling-based algorithm for ancestor sampling in particle Gibbs that mitigates this problem. Additionally, performance guarantees and a fallback strategy to prevent suffering from high rejection rates are discussed. The performance of the method is illustrated in two numerical examples.

AB - Particle Gibbs with ancestor sampling is an efficient and statistically principled algorithm for learning of dynamic systems. However, the ancestor sampling step used to improve mixing of the Markov chain requires the possibly expensive calculation of a set of ancestor weights for the complete particle system. In this paper, we propose a rejection-sampling-based algorithm for ancestor sampling in particle Gibbs that mitigates this problem. Additionally, performance guarantees and a fallback strategy to prevent suffering from high rejection rates are discussed. The performance of the method is illustrated in two numerical examples.

KW - Particle filters

KW - Particle Markov chain Monte Carlo

KW - Statistical learning

UR - http://www.scopus.com/inward/record.url?scp=85077707888&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2019.8918852

DO - 10.1109/MLSP.2019.8918852

M3 - Conference article in proceedings

AN - SCOPUS:85077707888

T3 - IEEE International Workshop on Machine Learning for Signal Processing

BT - Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019

PB - IEEE

T2 - IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 13 October 2019 through 16 October 2019

ER -

Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs

Abstrakti

Julkaisusarja

Workshop

Pääsy asiakirjaan

Muut tiedostot ja linkit

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