mcmcSampler: MCMC sampler

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

Description

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

Usage

mcmcSampler(myData, nIter, finalIterationPdf, modelVariance, mhPropRange, 
mhSinglePropRange, movesRange, startPoint, postPar, dName, timeScale, 
burn, iterPerPlotPrefix, priorParameters, L = 3, LRange, tau, 
gammaParameter, saveTheta, Prior = "complexity")

Arguments

myData	observed data.
nIter	number of mcmc iterations
finalIterationPdf	output folder
modelVariance	null
mhPropRange	positive integer
mhSinglePropRange	positive integer
movesRange	null
startPoint	positive integer
postPar	list of emprirically estimated parameters
dName	subject ID
timeScale	null
burn	burn-in period.
iterPerPlotPrefix	null
priorParameters	prior parameters.
L	null
LRange	range of possible values of the number of change-points.
tau	real.
gammaParameter	real.
saveTheta	TRUE.
Prior	character.

Value

MCMC output.

Author(s)

Panagiotis Papastamoulis

beast documentation built on May 2, 2019, 1:19 p.m.