Bayesian Multiple Change-Point Estimation and Segmentation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김재희 | - |
dc.contributor.author | 전수영 | - |
dc.date.accessioned | 2021-09-06T07:57:50Z | - |
dc.date.available | 2021-09-06T07:57:50Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/105220 | - |
dc.description.abstract | This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 한국통계학회 | - |
dc.title | Bayesian Multiple Change-Point Estimation and Segmentation | - |
dc.title.alternative | Bayesian Multiple Change-Point Estimation and Segmentation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 전수영 | - |
dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.20, no.6, pp.439 - 454 | - |
dc.relation.isPartOf | Communications for Statistical Applications and Methods | - |
dc.citation.title | Communications for Statistical Applications and Methods | - |
dc.citation.volume | 20 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 439 | - |
dc.citation.endPage | 454 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001820322 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | BIC | - |
dc.subject.keywordAuthor | multiple change-points | - |
dc.subject.keywordAuthor | segmentation | - |
dc.subject.keywordAuthor | stochastic approximation Monte Carlo. | - |
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