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
Bayesian training of neural networks using genetic programming
Pattern Recognition Letters
Joint segmentation of wind speed and direction using a hierarchical model
Computational Statistics & Data Analysis
Real-Time Model-Based Fault Detection and Isolation for UGVs
Journal of Intelligent and Robotic Systems
Hierarchical Bayesian sparse image reconstruction with application to MRFM
IEEE Transactions on Image Processing
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
IEEE Transactions on Signal Processing
Computer Methods and Programs in Biomedicine
Lidar waveform modeling using a marked point process
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Bayesian orthogonal component analysis for sparse representation
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
A marked point process for modeling lidar waveforms
IEEE Transactions on Image Processing
IEEE Transactions on Signal Processing
Efficient Bayesian analysis of multiple changepoint models with dependence across segments
Statistics and Computing
Data based segmentation and summarization for sensor data in semiconductor manufacturing
Expert Systems with Applications: An International Journal
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We propose some Bayesian methods to address the problem of fitting a signal modeled by a sequence of piecewise constant linear (in the parameters) regression models, for example, autoregressive or Volterra models. A joint prior distribution is set up over the number of the changepoints/knots, their positions, and over the orders of the linear regression models within each segment if these are unknown. Hierarchical priors are developed and, as the resulting posterior probability distributions and Bayesian estimators do not admit closed-form analytical expressions, reversible jump Markov chain Monte Carlo (MCMC) methods are derived to estimate these quantities. Results are obtained for standard denoising and segmentation of speech data problems that have already been examined in the literature. These results demonstrate the performance of our methods