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Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series

Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2019) <doi:10.1515/ijb-2018-0052> for a detailed presentation of the method.

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VersionRepositoryFileSize
1.2 rolling linux/jammy R-4.5 beast_1.2.tar.gz 300.0 KiB
1.2 rolling linux/noble R-4.5 beast_1.2.tar.gz 299.9 KiB
1.2 rolling source/ R- beast_1.2.tar.gz 217.8 KiB
1.2 latest linux/jammy R-4.5 beast_1.2.tar.gz 300.0 KiB
1.2 latest linux/noble R-4.5 beast_1.2.tar.gz 299.9 KiB
1.2 latest source/ R- beast_1.2.tar.gz 217.8 KiB
1.2 2026-04-26 source/ R- beast_1.2.tar.gz 217.8 KiB
1.2 2026-04-23 source/ R- beast_1.2.tar.gz 217.8 KiB
1.2 2026-04-09 windows/windows R-4.5 beast_1.2.zip 303.1 KiB
1.1 2025-04-20 source/ R- beast_1.1.tar.gz 216.8 KiB

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