Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/95724
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dc.contributor.authorLian, Hengen
dc.date.accessioned2013-07-23T01:49:33Zen
dc.date.accessioned2019-12-06T19:20:22Z-
dc.date.available2013-07-23T01:49:33Zen
dc.date.available2019-12-06T19:20:22Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationLian, H. (2012). Stochastic adaptation of importance sampler. Statistics, 46(6), 777-785.en
dc.identifier.urihttps://hdl.handle.net/10356/95724-
dc.identifier.urihttp://hdl.handle.net/10220/11998en
dc.description.abstractImproving efficiency of the importance sampler is at the centre of research on Monte Carlo methods. While the adaptive approach is usually not so straightforward within the Markov chain Monte Carlo framework, the counterpart in importance sampling can be justified and validated easily. We propose an iterative adaptation method for learning the proposal distribution of an importance sampler based on stochastic approximation. The stochastic approximation method can recruit general iterative optimization techniques like the minorization–maximization algorithm. The effectiveness of the approach in optimizing the Kullback divergence between the proposal distribution and the target is demonstrated using several examples.en
dc.language.isoenen
dc.relation.ispartofseriesStatisticsen
dc.rights© 2012 Taylor & Francis.en
dc.titleStochastic adaptation of importance sampleren
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen
dc.identifier.doi10.1080/02331888.2011.555549en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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