Sticky proposal densities for adaptive MCMC methods
| Autor: | David Luengo, Roberto Casarin, Luca Martino |
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| Rok vydání: | 2016 |
| Předmět: |
Computer science
Matemáticas Monte Carlo method Bayesian probability Adaptive MCMC Bayesian signal processing Monte Carlo methods Statistical inference Settore SECS-P/05 - Econometria 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Hybrid Monte Carlo 010104 statistics & probability symbols.namesake 0202 electrical engineering electronic engineering information engineering 0101 mathematics Markov chain business.industry 020206 networking & telecommunications Markov chain Monte Carlo symbols Artificial intelligence Settore SECS-S/01 - Statistica business Particle filter computer Algorithm Monte Carlo molecular modeling |
| Zdroj: | 2016 IEEE Statistical Signal Processing Workshop (SSP) | 2016 IEEE Workshop on Statistical Signal Processing | 26/06/2016-29/06/2016 | Palma de Mallorca (España) Archivo Digital UPM instname SSP |
| Popis: | Monte Carlo (MC) methods are commonly used in Bayesian signal processing to address complex inference problems. The performance of any MC scheme depends on the similarity between the proposal (chosen by the user) and the target (which depends on the problem). In order to address this issue, many adaptive MC approaches have been developed to construct the proposal density iteratively. In this paper, we focus on adaptive Markov chain MC (MCMC) algorithms, introducing a novel class of adaptive proposal functions that progressively “stick” to the target. This proposed class of sticky MCMC methods converge very fast to the target, thus being able to generate virtually independent samples after a few iterations. Numerical simulations illustrate the excellent performance of the sticky proposals when compared to other adaptive and non-adaptive schemes. |
| Databáze: | OpenAIRE |
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