beast
1.2Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series
Overview
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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- RR 4.6.0 released · 2026-04-24
- 1.2Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 1.12018-03-16 · diff ↗
- 1.02017-11-29
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-11-29
- Total releases
- 3 / 9 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 192 KB / 1 file
- Download size
- 217 KB
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