Parallel MCMC algorithm for Bayesian system identification

Title: Parallel MCMC algorithm for Bayesian system identification
Creator: Tran, Khoa T.; Ninness, Brett
Relation: 2015 54th IEEE Conference on Decision and Control (CDC). Proceedings of the 2015 54th IEEE Conference on Decision and Control (CDC) (Osaka, Japan 15-18 December, 2015) p. 2438-2443
Publisher Link: http://dx.doi.org/10.1109/CDC.2015.7402573
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2016
Description: A generalised framework for Metropolis-Hastings admits many algorithms as specialisations and allows for synthesis of multiple methods to create a parallel algorithm, with no tuning required, to efficiently draw uncorrelated samples, from the posterior density in Bayesian systems identification, at lower computational cost in comparison with conventional samplers. Two automatic annealing schemes demonstrate complementary robustness in detecting multi-modal distribution.
Subject: simulated annealing; Bayes methods; identification; Markov processes; Monte Carlo methods; parallel algorithms
Identifier: http://hdl.handle.net/1959.13/1318606
Identifier: uon:23652
Identifier: ISBN:9781479978861
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format