Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.478942
Title: Mode jumping in MCMC
Author: Behrens, Gundula Ragna
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2008
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Markov Chain Monte Carlo (MCMC) methods often have difficulties in moving between isolated modes. To understand these difficulties, some MCMC theory and some mode jumping approaches will be reviewed, first in fixed dimension and later in variable dimension. The focus will lie on improving the eficiency of the powerful, but computationally expensive method "tempered transitions". A technique for optimising the method's parameters ("temperatures") will be proposed. It will be demonstrated that the default choice of geometric temperatures can be far from optimal. The tuning technique will then be tested on a hard applied sampling problem, namely on sampling from a fixed-dimensional mixture model. The results will show that the optimisation is robust and performs well and that tempered transitions achieves mode jumping ("label-switching") where standard MCMC fails. Since mixture models are often of variable dimension, it will be verified that tempered transitions and the tuning technique can also be applied in variable-dimensional problems. Tests on a variable-dimensional mixture model will confirm that tempered transitions also improves jumps between dimensions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.478942  DOI: Not available
Keywords: QA Mathematics
Share: