Assessment of MCMC convergence: a time series and dynamical systems approach

Rodney C. Wolff, Darfiana Nur, Kerrie L. Mengersen

Research output: Contribution to conferencePaper

Abstract

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. In the present paper, 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 brief comments on analytical results, obtained via Edgeworth expansion, are also made.

Original languageEnglish
Pages46-49
Number of pages4
DOIs
Publication statusPublished - 2001
Externally publishedYes
Event2001 IEEE Workshop on Statitical Signal Processing Proceedings - Singapore, Singapore
Duration: 6 Aug 20018 Aug 2001

Conference

Conference2001 IEEE Workshop on Statitical Signal Processing Proceedings
CountrySingapore
CitySingapore
Period6/08/018/08/01

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    Wolff, R. C., Nur, D., & Mengersen, K. L. (2001). Assessment of MCMC convergence: a time series and dynamical systems approach. 46-49. Paper presented at 2001 IEEE Workshop on Statitical Signal Processing Proceedings, Singapore, Singapore. https://doi.org/10.1109/SSP.2001.955218