Surveying the Characteristics of Population Monte Carlo
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2013, Vol 7, Issue 9
Abstract
The importance sampling method as other Monte Carlo Markov Chain (MCMC) algorithms is iterative while this algorithm i.e. importance sampling does not depend on the initiating point. The Population Monte Carlo method includes the frequent production of importance sampling whose used importance functions depend on the previous produced importance samples. The advantage of this method over the MCMC algorithm is that the framework of this algorithm in each iteration is unbiased, so running this algorithm can stop at any given time. The reason is that the iterations improve running the importance function (i.e. the proposal distribution). Hence, this leads to the improved importance sampling. In this study, we survey this method through diverse examples.
Authors and Affiliations
Ehsan Fayyazi *| Department of Statistics, Science and Research Branch, Islamic Azad University, Fars, Iran. Department of Statistics, Science and Research Branch, Islamic Azad University, Fars, Iran., Gholamhossein Gholami| Department of Mathematics, Faculty of sciences, Urmia University, Urmia, Iran.
Keywords
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