Sticky proposal densities for adaptive MCMC methods

Martino, Luca and Casarin, R. and Luengo García, David (2016). Sticky proposal densities for adaptive MCMC methods. In: "2016 IEEE Workshop on Statistical Signal Processing", 26/06/2016 - 29/06/2016, Palma de Mallorca (España). ISBN 978-1-4673-7802-4. pp. 1-5.


Title: Sticky proposal densities for adaptive MCMC methods
  • Martino, Luca
  • Casarin, R.
  • Luengo García, David
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2016 IEEE Workshop on Statistical Signal Processing
Event Dates: 26/06/2016 - 29/06/2016
Event Location: Palma de Mallorca (España)
Title of Book: 2016 IEEE Statistical Signal Processing Workshop (SSP)
Date: 2016
ISBN: 978-1-4673-7802-4
Freetext Keywords: Statistical inference, Bayesian signal processing, Monte Carlo methods, adaptive MCMC
Faculty: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Department: Teoría de la Señal y Comunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

Government of SpainTEC2012-38058-C03-01DISSECTUnspecifiedUnspecified
Government of SpainTEC2015-64835-C3-3-RMIMOD-PLCUnspecifiedUnspecified
FP7SYRTO-SSH-2012- 320270UnspecifiedUnspecifiedUnspecified
FP7SYRTO-SSH-2012- 630677UnspecifiedUnspecifiedUnspecified

More information

Item ID: 46529
DC Identifier:
OAI Identifier:
DOI: 10.1109/SSP.2016.7551746
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 01 Feb 2018 19:30
Last Modified: 30 Jun 2018 22:30
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