Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms

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A4 Artikkeli konferenssijulkaisussa
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Date

2021-11-15

Department

Department of Electrical Engineering and Automation

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Mcode

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Language

en

Pages

6
1-6

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

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.

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Keywords

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

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DOI

10.1109/MLSP52302.2021.9596301

Citation

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

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https://urn.fi/URN:NBN:fi:aalto-202201261454

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[article-cris] Sähkötekniikan korkeakoulu / ELEC