Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems

DOI10.1007/978-3-7908-2604-3_57zbMath1436.62404arXiv1004.4347OpenAlexW3021988133MaRDI QIDQ3298515

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Publication date: 14 July 2020

Published in: Proceedings of COMPSTAT'2010 (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1004.4347

zbMATH Keywords

change-point detectionposterior distribution of change-points

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Non-Markovian processes: hypothesis testing (62M07)

Cites Work

• Detecting multiple change-points in the mean of Gaussian process by model selection
• Using penalized contrasts for the change-point problem
• Gaussian model selection with an unknown variance
• The log likelihood ratio in segmented regression
• Minimal penalties for Gaussian model selection
• Bootstrapping confidence intervals for the change-point of time series
• Multiple changepoint fitting via quasilikelihood, with application to DNA sequence segmentation
• A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data

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