BayesCPclust (version 0.1.0)

qn0_mk: Mixing probability for creating new cluster per bin

Description

Mixing probability for creating new cluster per bin

Usage

qn0_mk(w, m0, bs, as, M, km, lambda, mk, Yn, kstar)

Value

A numerical value representing the mixing value term used to compute the probability that the given data sequence should be a singleton cluster for a given bin size.

Arguments

w

A scalar representing the minimum number of points in each interval between two change points

m0

A scalar for the number of positions available to define change-points positions

bs

The hyperparameter value for the scale parameter in the inverse-gamma prior for the variance component

as

The hyperparameter value for the shape parameter in the inverse-gamma prior for the variance component

M

A scalar representing the number of points available for each data sequence

km

A scalar for the number of changes points in a cluster

lambda

A scalar defining the parameter for the Truncate Poisson distribution that controls the number of change points (or its initial values)

mk

A matrix with all possible values to distribute between change points

Yn

A vector with a data sequence

kstar

A scalar with the number maximum of change points in all clusters

See Also

[qn0()], [gibbs_alg()]

Examples

data(data)
M <- 50; k <- 0; w <- 10;
m0 <- M - 1 -(k+1)*w
for(k in 0:2){
mk <- RcppAlgos::permuteGeneral(0:m0, k + 1,
constraintFun = "sum",
comparisonFun = "==", limitConstraints = m0,
repetition = TRUE)}
out <- qn0_mk(w = 10, m0 = m0, bs = 1000, as = 2, M = 50, km = 1,
 lambda = 2, mk = mk[1,], Yn = data[,1], kstar = 2)