mcmcSampler

myData

nIter

finalIterationPdf

modelVariance

mhPropRange

mhSinglePropRange

movesRange

startPoint

list of emprirically estimated parameters

postPar

dName

timeScale

burn

iterPerPlotPrefix

priorParameters

range of possible values of the number of change-points.

LRange

gammaParameter

saveTheta

Prior

This function implements the Metropolis-Hastings MCMC sampler for individual 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.

mcmcSampler: MCMC sampler

Description

Usage

Arguments

Value