A survey of Monte Carlo methods for parameter estimation

A survey of Monte Carlo methods for parameter estimation

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 (MA...

Full description

Bibliographic Details
Main Authors: David Luengo, Luca Martino, Mónica Bugallo, Víctor Elvira, Simo Särkkä
Format: Article
Language:English
Published: SpringerOpen 2020-05-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Statistical signal processing
Bayesian inference
Monte Carlo methods
Metropolis-Hastings algorithm
Gibbs sampler
MH-within-Gibbs
Online Access:http://link.springer.com/article/10.1186/s13634-020-00675-6

Internet

http://link.springer.com/article/10.1186/s13634-020-00675-6

Similar Items