- Bayesian Anal.
- Volume 6, Number 4 (2011), 501-528.
Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments
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.
Bayesian Anal., Volume 6, Number 4 (2011), 501-528.
First available in Project Euclid: 13 June 2012
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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
- Paul Fearnhead. Comment on Article by Wyse et al. Bayesian Anal., Vol. 6, Iss 4 (2011), 529-532.
- Gary Koop. Comment on Article by Wyse et al. Bayesian Anal., Vol. 6, Iss. 4 (2011), 533-540 .
- Jason Wyse, Nial Friel, Håvard Rue. Rejoinder: "Comment on Article by Wyse et al.". Bayesian Anal., Vol. 6, Iss. 4 (2011), 541-546.