Bayesian Analysis

Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments

Jason Wyse, Nial Friel, and Håvard Rue

Full-text: Open access

Abstract

This paper proposes approaches for the analysis of multiple changepoint models when dependency in the data is modelled through a hierarchical Gaussian Markov random field. Integrated nested Laplace approximations are used to approximate data quantities, and an approximate filtering recursions approach is proposed for savings in compuational cost when detecting changepoints. All of these methods are simulation free. Analysis of real data demonstrates the usefulness of the approach in general. The new models which allow for data dependence are compared with conventional models where data within segments is assumed independent.

Article information

Source
Bayesian Anal., Volume 6, Number 4 (2011), 501-528.

Dates
First available in Project Euclid: 13 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1339616532

Digital Object Identifier
doi:10.1214/11-BA620

Mathematical Reviews number (MathSciNet)
MR2869956

Zentralblatt MATH identifier
1330.62161

Keywords
Changepoints Gaussian Markov random field Integrated Nested Laplace Approximation (INLA) approximate inference model selection

Citation

Wyse, Jason; Friel, Nial; Rue, Håvard. Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments. Bayesian Anal. 6 (2011), no. 4, 501--528. doi:10.1214/11-BA620. https://projecteuclid.org/euclid.ba/1339616532


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