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sampleBN = function(y, X, N, particles, priorList){
# Given the arguments, this function returns a population of MC draws for the values of the variable B, in the p-variate Normal regression model.
n = nrow(y)
p = ncol(y)
k = ncol(X)
B = array(0, c(k, p, N))
log.dB = numeric(N)
#
G = particles$G
# psi = particles$psi
# v = particles$v
z = particles$z
for(iN in 1:N){
# viN = v[iN,]
# ziN = z[iN,]
GiN = matrix(G[iN,,drop=F], ncol=p)
# ViN = diag(viN)
# absz.sqrtv = abs(ziN) / sqrt(viN)
# HiN = y - matrix(rep(psi[iN,],n),ncol=p,byrow=T) * matrix(rep(absz.sqrtv,p),ncol=p)
S.iN = t(X) %*% X
invS.iN = solve(S.iN)
Cpsi.iN = t(X) %*% y
M.iN = as.numeric(invS.iN %*% Cpsi.iN)
# Sigma.temp = kronecker(invS.iN, GiN)
Sigma.temp = kronecker(GiN, invS.iN)
Sigma.iN = (Sigma.temp + t(Sigma.temp)) / 2 # force symmetry
Bvec = as.numeric(rmnorm(1, M.iN, Sigma.iN))
# Bvec = as.numeric(rmvnorm(1, M.iN, Sigma.iN))
B[,,iN] = matrix(Bvec, ncol=p)
log.dB[iN] = dmnorm(Bvec, M.iN, Sigma.iN, log=T)
# log.dB[iN] = dmvnorm(Bvec, M.iN, Sigma.iN, log=T)
}
return(list(values=B, log.dq=log.dB))
}
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