Bayesian Changepoint Analysis

2020/06/18 by Tobias Siems, Siems, Tobias
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2006.10428

openalex publication_date 2020/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In my PhD thesis, we elaborate upon Bayesian changepoint analysis, whereby our focus is on three big topics: approximate sampling via MCMC, exact inference and uncertainty quantification. Besides, modeling matters are discussed in an ongoing fashion. Our findings are underpinned through several changepoint examples with a focus on a well-log drilling data.

Citations

• Bayesian Online Changepoint Detection
• Exact and efficient Bayesian inference for multiple changepoint problems
• Optimal Detection of Changepoints With a Linear Computational Cost
• Computing and Graphing Highest Density Regions
• Hidden semi-Markov models
• Maximum likelihood estimates of linear dynamic systems
• Monte Carlo sampling methods using Markov chains and their applications

Related

• A Bayesian framework for change-point detection with uncertainty quantification
• Changepoint Methods in Climatology
• Bayesian computation: a perspective on the current state, and sampling backwards and forwards
• Objective Bayesian Analysis for Change Point Problems
• Linking Frequentist and Bayesian Change-Point Methods