Assessment of MCMC convergence: a time series dynamical systems approach

Title: Assessment of MCMC convergence: a time series dynamical systems approach
Creator: Wolff, R. C.; Nur, D.; Mengersen, K. L.
Relation: 11th IEEE Signal Processing Workshop on Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing : 6th-8th August 2001, Singapore (Singapore 6th-8th August 2001) p. 46-49
Publisher Link: http://dx.doi.org/10.1109/SSP.2001.955218
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2001
Description: Important in the application of Markov chain Monte Carlo (MCMC) methods is the determination that a search run has converged. Given that such searches typically take place in high-dimensional spaces, there are many pitfalls and difficulties in making such assessments. We discuss the use of phase randomisation as tool in the MCMC context, provide some details of its distributional properties for time series which enable its use as a convergence diagnostic, and contrast its performance with a selection of other widely used diagnostics. Some comments on analytical results, obtained via Edgeworth expansion, are also made.
Subject: Markov chain Monte Carlo (MCMC); phase randomisation
Identifier: http://hdl.handle.net/1959.13/32079
Identifier: uon:2909
Identifier: ISBN:0780370112
Language: eng
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