Projekteja vuodessa
Abstrakti
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.
Alkuperäiskieli  Englanti 

Artikkeli  25 
Julkaisu  Eurasip Journal on Advances in Signal Processing 
Vuosikerta  2020 
Numero  1 
DOI  pysyväislinkit  
Tila  Julkaistu  1 joulukuuta 2020 
OKMjulkaisutyyppi  A2 Arvio tiedejulkaisuussa (artikkeli) 
Sormenjälki Sukella tutkimusaiheisiin 'A survey of Monte Carlo methods for parameter estimation'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.
Projektit
 1 Päättynyt

Sekventiaalisia Monte Carlo menetelmiä tila ja parametriestimointiin stokastisissa dynaamisissa systeemeissä
01/06/2015 → 31/08/2018
Projekti: Academy of Finland: Other research funding