Eye Tracking: dirichlet process
prior
Adapted from Congdon (2001), example 6.27, page 263.
model{
for( i in 1 : N ) {
S[i] ~ dcat(pi[])
mu[i] <- theta[S[i]]
x[i] ~ dpois(mu[i])
for (j in 1 : C) {
SC[i, j] <- equals(j, S[i])
}
}
# Precision Parameter
alpha <- 1
# alpha~ dgamma(0.1,0.1)
# Constructive DPP
p[1] <- r[1]
for (j in 2 : C) {
p[j] <- r[j] * (1 - r[j - 1]) * p[j -1 ] / r[j - 1]
}
p.sum <- sum(p[])
for (j in 1:C){
theta[j] ~ dgamma(A, B)
r[j] ~ dbeta(1, alpha)
# scaling to ensure sum to 1
pi[j] <- p[j] / p.sum
}
# hierarchical prior on theta[i] or preset parameters
A ~ dexp(0.1) B ~dgamma(0.1, 0.1)
# A <- 1 B <- 1
# total clusters
K <- sum(cl[])
for (j in 1 : C) {
sumSC[j] <- sum(SC[ , j])
cl[j] <- step(sumSC[j] -1)
}
}
Data ( click to open )
Results
a) fixed A and B, fixed alpha=1, C=10 (max catgeories)

Notice prior and data conflict.
b) variable A and B, fixed alpha=1, C=10 (max catgeories)