Andy and colleagues wrote the following in a 2000 paper in Applied Statistics:

When there is this logistic regression parameterization in the Stan manual:

```
data {
int<lower=0> N;
vector[N] x;
int<lower=0,upper=1> y[N];
}
parameters {
real alpha;
real beta;
}
model {
y ~ bernoulli_logit(alpha + beta * x);
}
```

to what extent is it safe to assume that the model is equivalent to a latent parameterization where there is an implied residual epsilon{i} for each observation, where the residuals are distributed with a variance of π^2/3, as also described in the Austin and Merlo tutorial here. @Bob_Carpenter @andrewgelman