Exploring the latent segmentation space for the assessment of multiple change-point models

• Published in: Computational statistics Vol. 28; no. 6; pp. 2641 - 2678
• Main Author: Guedon, Yann
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2013; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Analysis; By products; Computer Science; Data smoothing; Dynamic programming; Economic Theory/Quantitative Economics/Mathematical Methods; Markov analysis; Mathematics and Statistics; Modeling and Simulation; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Statistics; Studies; Dynamic programming algorithm; Latent structure model; Multiple change-point detection; Plant structure analysis; Smoothing algorithm; detection; latent; plant; multiple; dynamic; model; smoothing; analysis; change-point; programming; structure; algorithm

This paper addresses the retrospective or off-line multiple change-point detection problem. Multiple change-point models are here viewed as latent structure models and the focus is on inference concerning the latent segmentation space. Methods for exploring the space of possible segmentations of a s...