Fitting mixture importance sampling distributions via improved cross-entropy

Brereton, TJ; Chan, JCC; Kroese, DP

Fitting mixture importance sampling distributions via improved cross-entropy

Brereton, TJ Chan, JCC

Kroese, DP

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Publication Type:: Conference Proceeding
Citation:: Proceedings - Winter Simulation Conference, 2011, pp. 422 - 428
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Brereton, TJ	en_US
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.contributor.author	Kroese, DP	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - Winter Simulation Conference, 2011, pp. 422 - 428	en_US
dc.identifier.isbn	9781457721083	en_US
dc.identifier.issn	0891-7736	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119495
dc.description.abstract	In some rare-event settings, exponentially twisted distributions perform very badly. One solution to this problem is to use mixture distributions. However, it is difficult to select a good mixture distribution for importance sampling. We here introduce a simple adaptive method for choosing good mixture importance sampling distributions. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - Winter Simulation Conference	en_US
dc.relation.isbasedon	10.1109/WSC.2011.6147769	en_US
dc.title	Fitting mixture importance sampling distributions via improved cross-entropy	en_US
dc.type	Conference Proceeding
utslib.for	1403 Econometrics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In some rare-event settings, exponentially twisted distributions perform very badly. One solution to this problem is to use mixture distributions. However, it is difficult to select a good mixture distribution for importance sampling. We here introduce a simple adaptive method for choosing good mixture importance sampling distributions. © 2011 IEEE.

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http://hdl.handle.net/10453/119495