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http://hdl.handle.net/20.500.12358/21424
TitleMarkov Chain Monte Carlo Method and Perfect Simulation
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Abstract

Markov Chain Monte Carlo method is used to sample from complicated multivariate distribution with normalizing constants that may not be computable and from which direct sampling is not feasible. Recent years have seen the development of a new, exciting generation of Markov Chain Monte Carlo method: perfect simulation algorithms. In this thesis, we give a review of the new perfect simulation algorithms using Markov chains, focussed on the method called Coupling From The Past, since it allows not only an approximate but perfect (exact) simulation of the stationary distribution of finite state space Markov chain.

Authors
Karam, Arwah Noaman
Supervisors
Riffi, Mohammed
Typeرسالة ماجستير
Date2007
LanguageEnglish
Publisherthe islamic university
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  • PhD and MSc Theses- Faculty of Science [445]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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