Hi,

The GP model I have is bit unsolvable. Therefore I though about estimating only subset P1 of parameters first and then using resulting posterior as prior estimate remaining parameters P2. The model M1 to estimate P1 is:

`data { int<lower=1> N0; int<lower=0,upper=1> y0[N0]; } parameters { real<lower=0,upper=1> alpha; real<lower=0,upper=1> beta; vector<lower=0,upper=1>[N0] y0_raw; } model { y0_raw ~ beta(alpha, beta); y0 ~ bernoulli(y0_raw); }`

Some patients initially are sick(y0=0), some are healthy(y0=1). I am trying to represent the initial patient population just using parameters alpha and beta. My plan is to use the initial health status of patient population with alpha and beta. Right now they are difficult to interpret. I wonder if I could use some distribution instead of beta so the parameters are more interpretable and directly linked to the proportion of sick patients.

I appreciate any advice.