qn0_mk: Mixing probability for creating new cluster per bin

In BayesCPclust: A Bayesian Approach for Clustering Constant-Wise Change-Point Data

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)

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

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.

Note

This function is called within [qn0()]. It should not be called alone.

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)

BayesCPclust documentation built on April 4, 2025, 5:19 a.m.