computePosteriorParameters

Compute empirical posterior parameters

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.

Panagiotis Papastamoulis

beast

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

computePosteriorParameters function

<dl> <dt>myDataList</dt>
<dd>Observed data.</dd> <dt>priorParameters</dt>
<dd>Prior parameters.</dd></dl>

Arguments

Author

Compute empirical posterior parameters — computePosteriorParameters

<dl>

 <dt>myDataList</dt>
<dd>Observed data.</dd>

 <dt>priorParameters</dt>
<dd>Prior parameters.</dd>

</dl>

computePosteriorParameters: Compute empirical posterior parameters

Description

Usage

Value

Arguments

Author