Bayesian Multiple Change-Point Estimation and Segmentation

- Journal title : Communications for Statistical Applications and Methods
- Volume 20, Issue 6, 2013, pp.439-454
- Publisher : The Korean Statistical Society
- DOI : 10.5351/CSAM.2013.20.6.439

Title & Authors

Bayesian Multiple Change-Point Estimation and Segmentation

Kim, Jaehee; Cheon, Sooyoung;

Kim, Jaehee; Cheon, Sooyoung;

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.

Keywords

BIC;multiple change-points;segmentation;stochastic approximation Monte Carlo;

Language

English

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