Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jaehee | - |
dc.contributor.author | Cheon, Sooyoung | - |
dc.date.accessioned | 2021-09-08T02:50:01Z | - |
dc.date.available | 2021-09-08T02:50:01Z | - |
dc.date.created | 2021-06-11 | - |
dc.date.issued | 2010-06 | - |
dc.identifier.issn | 0943-4062 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/116366 | - |
dc.description.abstract | Bayesian multiple change-point models are built with data from normal, exponential, binomial and Poisson distributions with a truncated Poisson prior for the number of change-points and conjugate prior for the distributional parameters. We applied Annealing Stochastic Approximation Monte Carlo (ASAMC) for posterior probability calculations for the possible set of change-points. The proposed methods are studied in simulation and applied to temperature and the number of respiratory deaths in Seoul, South Korea. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER HEIDELBERG | - |
dc.subject | RANDOM-VARIABLES | - |
dc.subject | INFERENCE | - |
dc.subject | MODELS | - |
dc.subject | TIME | - |
dc.subject | DISTRIBUTIONS | - |
dc.subject | COMPUTATION | - |
dc.subject | EFFICIENT | - |
dc.subject | ALGORITHM | - |
dc.subject | POLLUTION | - |
dc.subject | SEQUENCE | - |
dc.title | Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cheon, Sooyoung | - |
dc.identifier.doi | 10.1007/s00180-009-0172-x | - |
dc.identifier.scopusid | 2-s2.0-84893710522 | - |
dc.identifier.wosid | 000276653900003 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL STATISTICS, v.25, no.2, pp.215 - 239 | - |
dc.relation.isPartOf | COMPUTATIONAL STATISTICS | - |
dc.citation.title | COMPUTATIONAL STATISTICS | - |
dc.citation.volume | 25 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 215 | - |
dc.citation.endPage | 239 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | RANDOM-VARIABLES | - |
dc.subject.keywordPlus | INFERENCE | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | TIME | - |
dc.subject.keywordPlus | DISTRIBUTIONS | - |
dc.subject.keywordPlus | COMPUTATION | - |
dc.subject.keywordPlus | EFFICIENT | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | POLLUTION | - |
dc.subject.keywordPlus | SEQUENCE | - |
dc.subject.keywordAuthor | Annealing Stochastic Approximation Monte Carlo (ASAMC) | - |
dc.subject.keywordAuthor | Bayesian change-point model | - |
dc.subject.keywordAuthor | Bayes factor | - |
dc.subject.keywordAuthor | BIC | - |
dc.subject.keywordAuthor | Posterior | - |
dc.subject.keywordAuthor | Truncated Poisson | - |
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