Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms

Simo Särkkä; Christos Merkatas; Toni Karvonen

doi:10.1109/MLSP52302.2021.9596301

Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms

Simo Särkkä, Christos Merkatas, Toni Karvonen

Alan Turing Institute

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

171 Lataukset (Pure)

Abstrakti

Markov chain Monte Carlo (MCMC) methods are a cornerstone of Bayesian inference and stochastic simulation. The Metropolis-adjusted Langevin algorithm (MALA) is an MCMC method that relies on the simulation of a stochastic differential equation (SDE) whose stationary distribution is the desired target density using the Euler-Maruyama algorithm and accounts for simulation errors using a Metropolis step. In this paper we propose a modification of the MALA which uses Gaussian assumed density approximations for the integration of the SDE. The effectiveness of the algorithm is illustrated on simulated and real data sets.

Alkuperäiskieli	Englanti
Otsikko	2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021
Kustantaja	IEEE
Sivut	1-6
Sivumäärä	6
ISBN (elektroninen)	978-1-7281-6338-3
ISBN (painettu)	978-1-6654-1184-4
DOI - pysyväislinkit	https://doi.org/10.1109/MLSP52302.2021.9596301
Tila	Julkaistu - 15 marrask. 2021
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Workshop on Machine Learning for Signal Processing - Gold Coast, Austraalia Kesto: 25 lokak. 2021 → 28 lokak. 2021 Konferenssinumero: 31 https://2021.ieeemlsp.org/

Julkaisusarja

Nimi	Machine learning for signal processing
ISSN (painettu)	1551-2541

Workshop

Workshop	IEEE International Workshop on Machine Learning for Signal Processing
Lyhennettä	MLSP
Maa/Alue	Austraalia
Kaupunki	Gold Coast
Ajanjakso	25/10/2021 → 28/10/2021
www-osoite	https://2021.ieeemlsp.org/

Pääsy asiakirjaan

10.1109/MLSP52302.2021.9596301

ELEC_Särkkä_etal_Gaussian_approximations_of_SDEs_MLSP2021Accepted author manuscript, 450 KB

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Linkki julkaisuun Scopuksessa

ADAFUME: Kehittyneitä datafuusiomenetelmiä ympäristön mallintamiseen
Särkkä, S. (Vastuullinen tutkija), Corenflos, A. (Projektin jäsen), Raitoharju, M. (Projektin jäsen), Gao, R. (Projektin jäsen), Merkatas, C. (Projektin jäsen), Sarmavuori, J. (Projektin jäsen), Yaghoobi, F. (Projektin jäsen), Ma, X. (Projektin jäsen) & Hassan, S. S. (Projektin jäsen)
01/01/2020 → 31/12/2023
Projekti: Academy of Finland: Other research funding

Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Siteeraa tätä

@inproceedings{7191685f7c4e4b6ab0abff5c8d59efbb,

title = "Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms",

abstract = "Markov chain Monte Carlo (MCMC) methods are a cornerstone of Bayesian inference and stochastic simulation. The Metropolis-adjusted Langevin algorithm (MALA) is an MCMC method that relies on the simulation of a stochastic differential equation (SDE) whose stationary distribution is the desired target density using the Euler-Maruyama algorithm and accounts for simulation errors using a Metropolis step. In this paper we propose a modification of the MALA which uses Gaussian assumed density approximations for the integration of the SDE. The effectiveness of the algorithm is illustrated on simulated and real data sets.",

keywords = "Monte Carlo methods, Machine learning algorithms, Heuristic algorithms, Signal processing algorithms, Machine learning, Signal processing, Markov processes",

author = "Simo S{\"a}rkk{\"a} and Christos Merkatas and Toni Karvonen",

year = "2021",

month = nov,

day = "15",

doi = "10.1109/MLSP52302.2021.9596301",

language = "English",

isbn = "978-1-6654-1184-4",

series = "Machine learning for signal processing",

publisher = "IEEE",

pages = "1--6",

booktitle = "2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021",

address = "United States",

note = "IEEE International Workshop on Machine Learning for Signal Processing , MLSP ; Conference date: 25-10-2021 Through 28-10-2021",

url = "https://2021.ieeemlsp.org/",

}

Särkkä, S, Merkatas, C & Karvonen, T 2021, Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms. julkaisussa 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021., 9596301, Machine learning for signal processing, IEEE, Sivut 1-6, IEEE International Workshop on Machine Learning for Signal Processing , Gold Coast, Austraalia, 25/10/2021. https://doi.org/10.1109/MLSP52302.2021.9596301

Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms. / Särkkä, Simo; Merkatas, Christos; Karvonen, Toni.
2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021. IEEE, 2021. s. 1-6 9596301 (Machine learning for signal processing).

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

TY - GEN

T1 - Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms

AU - Särkkä, Simo

AU - Merkatas, Christos

AU - Karvonen, Toni

N1 - Conference code: 31

PY - 2021/11/15

Y1 - 2021/11/15

N2 - Markov chain Monte Carlo (MCMC) methods are a cornerstone of Bayesian inference and stochastic simulation. The Metropolis-adjusted Langevin algorithm (MALA) is an MCMC method that relies on the simulation of a stochastic differential equation (SDE) whose stationary distribution is the desired target density using the Euler-Maruyama algorithm and accounts for simulation errors using a Metropolis step. In this paper we propose a modification of the MALA which uses Gaussian assumed density approximations for the integration of the SDE. The effectiveness of the algorithm is illustrated on simulated and real data sets.

AB - Markov chain Monte Carlo (MCMC) methods are a cornerstone of Bayesian inference and stochastic simulation. The Metropolis-adjusted Langevin algorithm (MALA) is an MCMC method that relies on the simulation of a stochastic differential equation (SDE) whose stationary distribution is the desired target density using the Euler-Maruyama algorithm and accounts for simulation errors using a Metropolis step. In this paper we propose a modification of the MALA which uses Gaussian assumed density approximations for the integration of the SDE. The effectiveness of the algorithm is illustrated on simulated and real data sets.

KW - Monte Carlo methods

KW - Machine learning algorithms

KW - Heuristic algorithms

KW - Signal processing algorithms

KW - Machine learning

KW - Signal processing

KW - Markov processes

UR - http://www.scopus.com/inward/record.url?scp=85122809655&partnerID=8YFLogxK

U2 - 10.1109/MLSP52302.2021.9596301

DO - 10.1109/MLSP52302.2021.9596301

M3 - Conference article in proceedings

SN - 978-1-6654-1184-4

T3 - Machine learning for signal processing

SP - 1

EP - 6

BT - 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021

PB - IEEE

T2 - IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 25 October 2021 through 28 October 2021

ER -

Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms

Abstrakti

Julkaisusarja

Workshop

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

ADAFUME: Kehittyneitä datafuusiomenetelmiä ympäristön mallintamiseen

Laitteet

Science-IT

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