Bayesian Changepoint Analysis

TL;DR

This paper discusses Bayesian changepoint analysis, focusing on approximate sampling, exact inference, and uncertainty quantification, demonstrated through real-world well-log drilling data examples.

Contribution

It advances Bayesian changepoint analysis by integrating approximate sampling, exact inference, and uncertainty quantification methods, with practical applications.

Findings

Effective MCMC sampling techniques demonstrated

Exact inference methods developed and validated

Uncertainty quantification applied to real data

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.