evaluatePrior: evaluate Prior

In bpgmm: Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models

evaluate Prior

Description

evaluate prior value for parameter Theta and Y.

Usage

evaluatePrior(m, p, muBar, hparam, thetaYList, ZOneDim, qVec, constraint,
  clusInd)

Arguments

m	m
p	p
muBar	mu_bar
hparam	hyper parameter class
thetaYList	theta Y list
ZOneDim	one dim of z
qVec	q vector
constraint	type of constraint
clusInd	cluster indicator vector

Examples

m <- 20
n <- 500
p <- 10
muBar <- c(
  -33.1342706763595, -35.2699639183419, 48.276928009445, 16.2370659601218,
  19.0023163870536, -23.4900965314972, 37.1081269873873, 4.74944562930846,
  4.6918997353449, -4.55088073255655
)
hparam <- new("Hparam",
  alpha1 = 0.567755037123148, alpha2 = 1.1870201935945,
  delta = 2, ggamma = 2, bbeta = 3.39466184520673
)
qVec <- c(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)
ZOneDim <- sample(seq_len(m), n, replace = TRUE)
constraint <- c(0, 0, 0)
thetaYList <- generatePriorThetaY(m, n, p, muBar, hparam, qVec, ZOneDim, constraint)
clusInd <- rep(1, m)
#'

evaluatePrior(
  m,
  p,
  muBar,
  hparam,
  thetaYList,
  ZOneDim,
  qVec,
  constraint,
  clusInd
)

bpgmm documentation built on June 2, 2022, 1:10 a.m.