Exact and efficient Bayesian inference for multiple changepoint problems
Statistics and Computing
Modeling changing dependency structure in multivariate time series
Proceedings of the 24th international conference on Machine learning
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Computational Statistics & Data Analysis
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Signal segmentation and modelling based on equipartition principle
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
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Efficient Bayesian analysis of multiple changepoint models with dependence across segments
Statistics and Computing
Multimodal segmentation of object manipulation sequences with product models
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Towards Non-Stationary Grid Models
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We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem.