Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
An application of MCMC methods for the multiple change-points problem
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
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Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Bayesian curve fitting using MCMC with applications to signalsegmentation
IEEE Transactions on Signal Processing
Exact Bayesian curve fitting and signal segmentation
IEEE Transactions on Signal Processing
Modeling changing dependency structure in multivariate time series
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Computational Statistics & Data Analysis
Change point detection based on call detail records
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Heuristic Bayesian Segmentation for Discovery of Coexpressed Genes within Genomic Regions
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bayesian online learning of the hazard rate in change-point problems
Neural Computation
Joint segmentation of multivariate Gaussian processes using mixed linear models
Computational Statistics & Data Analysis
Efficient Bayesian analysis of multiple changepoint models with dependence across segments
Statistics and Computing
A comparison of estimators for regression models with change points
Statistics and Computing
Emulating human observers with bayesian binning: Segmentation of action streams
ACM Transactions on Applied Perception (TAP)
Segmentation of action streams human observers vs. Bayesian binning
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
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ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
On-line changepoint detection and parameter estimation with application to genomic data
Statistics and Computing
Implied distributions in multiple change point problems
Statistics and Computing
Non-stationary bayesian networks based on perfect simulation
Proceedings of the 21st ACM international conference on Information and knowledge management
On-line bayesian context change detection in web service systems
Proceedings of the 2013 international workshop on Hot topics in cloud services
Object tracking within the framework of concept drift
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Exploring the latent segmentation space for the assessment of multiple change-point models
Computational Statistics
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We demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to work on product partition models. The computational complexity of the approach is quadratic in the number of observations, but an approximate version, which introduces negligible error, and whose computational cost is roughly linear in the number of observations, is also possible. Our approach can be useful, for example within an MCMC algorithm, even when the independence assumptions do not hold. We demonstrate our approach on coal-mining disaster data and on well-log data. Our method can cope with a range of models, and exact simulation from the posterior distribution is possible in a matter of minutes.