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December 2011 Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments
Jason Wyse, Nial Friel, Håvard Rue
Bayesian Anal. 6(4): 501-528 (December 2011). DOI: 10.1214/11-BA620

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.

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Jason Wyse. Nial Friel. Håvard Rue. "Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments." Bayesian Anal. 6 (4) 501 - 528, December 2011. https://doi.org/10.1214/11-BA620

Information

Published: December 2011
First available in Project Euclid: 13 June 2012

zbMATH: 1330.62161
MathSciNet: MR2869956
Digital Object Identifier: 10.1214/11-BA620

Rights: Copyright © 2011 International Society for Bayesian Analysis

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Vol.6 • No. 4 • December 2011
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