Bayesian Online Changepoint Detection. Adams, R. P. & MacKay, D. J. C. *arXiv:0710.3742 [stat]*, October, 2007. arXiv: 0710.3742Paper abstract bibtex Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.

@article{adams_bayesian_2007,
title = {Bayesian {Online} {Changepoint} {Detection}},
url = {http://arxiv.org/abs/0710.3742},
abstract = {Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.},
urldate = {2020-10-02},
journal = {arXiv:0710.3742 [stat]},
author = {Adams, Ryan Prescott and MacKay, David J. C.},
month = oct,
year = {2007},
note = {arXiv: 0710.3742},
keywords = {Statistics - Machine Learning},
}

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