Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities
DC Field | Value | Language |
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dc.contributor.author | Cheon, Sooyoung | - |
dc.contributor.author | Liang, Faming | - |
dc.contributor.author | Chen, Yuguo | - |
dc.contributor.author | Yu, Kai | - |
dc.date.accessioned | 2021-09-05T07:32:42Z | - |
dc.date.available | 2021-09-05T07:32:42Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2014-07 | - |
dc.identifier.issn | 0960-3174 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/98161 | - |
dc.description.abstract | Importance sampling and Markov chain Monte Carlo methods have been used in exact inference for contingency tables for a long time, however, their performances are not always very satisfactory. In this paper, we propose a stochastic approximation Monte Carlo importance sampling (SAMCIS) method for tackling this problem. SAMCIS is a combination of adaptive Markov chain Monte Carlo and importance sampling, which employs the stochastic approximation Monte Carlo algorithm (Liang et al., J. Am. Stat. Assoc., 102(477):305-320, 2007) to draw samples from an enlarged reference set with a known Markov basis. Compared to the existing importance sampling and Markov chain Monte Carlo methods, SAMCIS has a few advantages, such as fast convergence, ergodicity, and the ability to achieve a desired proportion of valid tables. The numerical results indicate that SAMCIS can outperform the existing importance sampling and Markov chain Monte Carlo methods: It can produce much more accurate estimates in much shorter CPU time than the existing methods, especially for the tables with high degrees of freedom. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | WANG-LANDAU ALGORITHM | - |
dc.subject | CONTINGENCY-TABLES | - |
dc.subject | MARKOV BASES | - |
dc.subject | EFFICIENT | - |
dc.subject | OPTIMIZATION | - |
dc.subject | CONVERGENCE | - |
dc.subject | VALUES | - |
dc.title | Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cheon, Sooyoung | - |
dc.identifier.doi | 10.1007/s11222-013-9384-6 | - |
dc.identifier.scopusid | 2-s2.0-84957428406 | - |
dc.identifier.wosid | 000338275300002 | - |
dc.identifier.bibliographicCitation | STATISTICS AND COMPUTING, v.24, no.4, pp.505 - 520 | - |
dc.relation.isPartOf | STATISTICS AND COMPUTING | - |
dc.citation.title | STATISTICS AND COMPUTING | - |
dc.citation.volume | 24 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 505 | - |
dc.citation.endPage | 520 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | WANG-LANDAU ALGORITHM | - |
dc.subject.keywordPlus | CONTINGENCY-TABLES | - |
dc.subject.keywordPlus | MARKOV BASES | - |
dc.subject.keywordPlus | EFFICIENT | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | CONVERGENCE | - |
dc.subject.keywordPlus | VALUES | - |
dc.subject.keywordAuthor | Contingency table | - |
dc.subject.keywordAuthor | Exact inference | - |
dc.subject.keywordAuthor | Importance sampling | - |
dc.subject.keywordAuthor | MCMC | - |
dc.subject.keywordAuthor | Stochastic approximation | - |
dc.subject.keywordAuthor | Monte Carlo | - |
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