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

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

2010-11-23

arxiv.org/abs/1011.5038v2

Statistics

Computation

24 pages, 11 figures

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-587813