Bayesian Multiple Change-Point Estimation and SegmentationBayesian Multiple Change-Point Estimation and Segmentation
- Other Titles
- Bayesian Multiple Change-Point Estimation and Segmentation
- Authors
- 김재희; 전수영
- Issue Date
- 2013
- Publisher
- 한국통계학회
- Keywords
- BIC; multiple change-points; segmentation; stochastic approximation Monte Carlo.
- Citation
- Communications for Statistical Applications and Methods, v.20, no.6, pp.439 - 454
- Indexed
- KCI
- Journal Title
- Communications for Statistical Applications and Methods
- Volume
- 20
- Number
- 6
- Start Page
- 439
- End Page
- 454
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/105220
- ISSN
- 2287-7843
- 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.
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Collections - Graduate School > Department of Applied Statistics > 1. Journal Articles
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