# "sets up priors to be close to flat
#  on the mean and standard deviation of the beta
#  distribution"

data{
  zero <- 0
}

model{

  zero ~ dpois(phi)
  phi<- -log(1/pow(alpha+beta,power))

  alpha ~ dunif(min,max)
  beta ~ dunif(min,max)

  #for(i in 1:N){
  y ~ dbeta(alpha, beta)
  #}

  # meanp <- alpha/(alpha+beta)
  # varp <- sqrt(alpha*beta/(pow(alpha+beta,2)*(alpha+beta+1)))
}
