TY - JOUR

T1 - Efficient Bayesian analysis of multiple changepoint models with dependence across segments.

AU - Fearnhead, Paul

AU - Liu, Zhen

PY - 2011/4

Y1 - 2011/4

N2 - We consider Bayesian analysis of a class of multiple changepoint models. While there are a variety of efficient ways to analyse these models if the parameters associated with each segment are independent, there are few general approaches for models where the parameters are dependent. Under the assumption that the dependence is Markov, we propose an efficient online algorithm for sampling from an approximation to the posterior distribution of the number and position of the changepoints. In a simulation study, we show that the approximation introduced is negligible. We illustrate the power of our approach through fitting piecewise polynomial models to data, under a model which allows for either continuity or discontinuity of the underlying curve at each changepoint. This method is competitive with, or out-performs, other methods for inferring curves from noisy data; and uniquely it allows for inference of the locations of discontinuities in the underlying curve.

AB - We consider Bayesian analysis of a class of multiple changepoint models. While there are a variety of efficient ways to analyse these models if the parameters associated with each segment are independent, there are few general approaches for models where the parameters are dependent. Under the assumption that the dependence is Markov, we propose an efficient online algorithm for sampling from an approximation to the posterior distribution of the number and position of the changepoints. In a simulation study, we show that the approximation introduced is negligible. We illustrate the power of our approach through fitting piecewise polynomial models to data, under a model which allows for either continuity or discontinuity of the underlying curve at each changepoint. This method is competitive with, or out-performs, other methods for inferring curves from noisy data; and uniquely it allows for inference of the locations of discontinuities in the underlying curve.

KW - Changepoint detection – Particle filters – Sequential Monte Carlo – Segmentation – Wavelets – Well-log

U2 - 10.1007/s11222-009-9163-6

DO - 10.1007/s11222-009-9163-6

M3 - Journal article

VL - 21

SP - 217

EP - 229

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 2

ER -