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

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About

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 (2017) for a detailed presentation of the method.

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Key Metrics

Version 1.1
R ≥ 2.10
Published 2018-03-16 2204 days ago
Needs compilation? no
License GPL-2
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Maintainer

Maintainer

Panagiotis Papastamoulis

papapast@yahoo.gr

Authors

Panagiotis Papastamoulis

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

beast archive

Depends

R ≥ 2.10

Imports

RColorBrewer