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.
|Number of pages||4|
|Publication status||Published - 2001|
|Event||2001 IEEE Workshop on Statitical Signal Processing Proceedings - Singapore, Singapore|
Duration: 6 Aug 2001 → 8 Aug 2001
|Conference||2001 IEEE Workshop on Statitical Signal Processing Proceedings|
|Period||6/08/01 → 8/08/01|