I’m trying to create a Bayesian regression model for the purpose of parameter estimation. The outcome PROM that can take any value between 0 and 100. I initially created a model in jags

mod1_string = " model {

for (i in 1:length(y)) {

y[i] ~ dnorm(mu[i], 1/25)

mu[i] = int +

b_1 * continuous1[i] +

b_2 * continuous2[i] +

b[1] * ordinal1[i] +

b[2] * ordinal2[i] +

b[3] * ordinal3[i] +

b[4] * ordina4[i]

}

int ~ dnorm(-5, 1.0/25.0)

```
for (j in 1:10) {
b[j] ~ ddexp(0, sqrt(2))
}
b_1 ~ dnorm(0.06, 1/1)
b_2 ~ dnorm(0.03, 1/1)
```

} "

The values for my continuous priors were taken from the literature. My ordinal priors were set to be double exponentials centered on 0. What I would prefer to have is a model that is also bound by limits 0 to 100. Should I scale my outcome to be between 0 and 1 and then use a logit on mu? In which case how do I choose the distribution of Y?