# Is this JAGS model ok, and can it be made faster?

I'm very new to Bayesian analysis, and I've come up with the following model. My goal is to get for each individual "test unit" a distribution that describes the lift in success rate under one of several test conditions.

#data
N <- 50000 #Number of test units
T <- 2 #Number of test conditions
succs<-structure(c(...N x T...))
trials<-structure(c(...N x T...))

#model
model {
for (j in 1:T) {
TestCondition[j] ~ dnorm(0, TestCondition.tau)
}
for (i in 1:N) {
Unit[i] ~ dnorm(0, Unit.tau)
}
for (i in 1:N) {
for(j in 1:T){
succs[i,j] ~ dbin(p[i,j],trials[i,j])
eps[i,j] ~ dnorm(0, eps.tau)
logit(p[i,j]) <- mu + Unit[i] + TestCondition[j] + eps[i,j]
for(k in 1:j-1){ #Only compute pairwise when non-redundant
delta[i,(j-1)*(j-2)/2+k] <- p[i,j]-p[i,k]
}
}
}
mu ~ dlogis(0, 1)
TestCondition.tau ~ dgamma(2,2)
Unit.tau ~ dgamma(2,2)
eps.tau ~ dgamma(2,1)
}


1) Is the above model appropriate for the problem I'm trying to solve?

2) The model takes a long time for large datasets (which is basically all of my datasets). Are there any ways to reformulate the model so it can handle large datasets better?