beast: 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 (2017) <arXiv:1709.06111> for a detailed presentation of the method.
| Version: |
1.1 |
| Depends: |
R (≥ 2.10) |
| Imports: |
RColorBrewer |
| Published: |
2018-03-16 |
| Author: |
Panagiotis Papastamoulis |
| Maintainer: |
Panagiotis Papastamoulis <papapast at yahoo.gr> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Citation: |
beast citation info |
| CRAN checks: |
beast results |
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