A survey of Monte Carlo methods for parameter estimation

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Journal Title
Journal ISSN
Volume Title
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
Date
2020-05-29
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Eurasip Journal on Advances in Signal Processing, Volume 2020, issue 1
Abstract
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and the Monte Carlo (MC) methodology is one feasible approach. MC methods proceed by drawing random samples, either from the desired distribution or from a simpler one, and using them to compute consistent estimators. The most important families of MC algorithms are the Markov chain MC (MCMC) and importance sampling (IS). On the one hand, MCMC methods draw samples from a proposal density, building then an ergodic Markov chain whose stationary distribution is the desired distribution by accepting or rejecting those candidate samples as the new state of the chain. On the other hand, IS techniques draw samples from a simple proposal density and then assign them suitable weights that measure their quality in some appropriate way. In this paper, we perform a thorough review of MC methods for the estimation of static parameters in signal processing applications. A historical note on the development of MC schemes is also provided, followed by the basic MC method and a brief description of the rejection sampling (RS) algorithm, as well as three sections describing many of the most relevant MCMC and IS algorithms, and their combined use. Finally, five numerical examples (including the estimation of the parameters of a chaotic system, a localization problem in wireless sensor networks and a spectral analysis application) are provided in order to demonstrate the performance of the described approaches.
Description
ERC 647423 (FI ei partnerilistalla CORDIS) / mm
Keywords
Adaptive MCMC, Bayesian inference, Gibbs sampler, Importance sampling, Metropolis-Hastings algorithm, MH-within-Gibbs, Monte Carlo methods, Population Monte Carlo, Statistical signal processing
Other note
Citation
Luengo, D, Martino, L, Bugallo, M, Elvira, V & Särkkä, S 2020, ' A survey of Monte Carlo methods for parameter estimation ', Eurasip Journal on Advances in Signal Processing, vol. 2020, no. 1, 25 . https://doi.org/10.1186/s13634-020-00675-6