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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 multivariate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multidimensional integration, as in the minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for these estimators cannot be found in most realworld 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.
Original language  English 

Article number  25 
Number of pages  62 
Journal  Eurasip Journal on Advances in Signal Processing 
Volume  2020 
Issue number  1 
DOIs  
Publication status  Published  29 May 2020 
MoE publication type  A2 Review article in a scientific journal 
Keywords
 Adaptive MCMC
 Bayesian inference
 Gibbs sampler
 Importance sampling
 MetropolisHastings algorithm
 MHwithinGibbs
 Monte Carlo methods
 Population Monte Carlo
 Statistical signal processing
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Projects
 1 Finished

Sequential Monte Carlo Methods for State and Parameter Estimation in Stochastic Dynamic Systems
01/06/2015 → 31/08/2018
Project: Academy of Finland: Other research funding