Orthogonal MCMC algorithms

Martino, Luca and Elvira Arregui, Víctor and Luengo García, David and Artés Rodríguez, Antonio and Corander, Jukka (2014). Orthogonal MCMC algorithms. In: "2014 IEEE Workshop on Statistical Signal Processing (SSP 14)", 29/06/2014 - 02/07/2014, Gold Coast (Australia). ISBN 978-1-4799-4975-5. pp. 364-367.


Title: Orthogonal MCMC algorithms
  • Martino, Luca
  • Elvira Arregui, Víctor
  • Luengo García, David
  • Artés Rodríguez, Antonio
  • Corander, Jukka
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2014 IEEE Workshop on Statistical Signal Processing (SSP 14)
Event Dates: 29/06/2014 - 02/07/2014
Event Location: Gold Coast (Australia)
Title of Book: 2014 IEEE Workshop on Statistical Signal Processing (SSP)
Date: 2014
ISBN: 978-1-4799-4975-5
Freetext Keywords: Markov Chain Monte Carlo (MCMC), Parallel Chains, Population Monte Carlo, Bayesian inference.
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 widely used in signal processing, machine learning and stochastic optimization. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel ?vertical? chains are led by random-walk proposals, whereas the ?horizontal? MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.

Funding Projects

Government of SpainCOMONSENS (CSD2008-00010)UnspecifiedUnspecifiedUnspecified
Government of SpainALCIT (TEC2012-38800-C03-01)UnspecifiedUnspecifiedUnspecified
Government of SpainDIS- SECT (TEC2012-38058-C03-01)UnspecifiedUnspecifiedUnspecified
Government of SpainCOMPREHENSION (TEC2012-38883- C02-01)UnspecifiedUnspecifiedUnspecified

More information

Item ID: 36434
DC Identifier: http://oa.upm.es/36434/
OAI Identifier: oai:oa.upm.es:36434
Official URL: http://www.ee.unimelb.edu.au/SSP2014/
Deposited by: Memoria Investigacion
Deposited on: 28 Mar 2016 16:53
Last Modified: 28 Mar 2016 16:53
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