Hello,

I am driving the full conditional of β

Y follows Bernoulli distribution (p)

Convolution model is given as: logit(p)=X'B+u+v ;

u is spatial random effects and v is non spatial random effects

The posterior distribution is given as:

Ṗ(u, v, K, λ, β І y) ≈Likelihood*structured car prior* unstructured exchangeable prior* Normal priors* hyper priors

Likelihood=∏ (n_iCyi)pi^yi (1-pi)^n_i -yi

n_iCyi is combination relation

My question is,  what I have to replace by this likelihood  if I want to drive the full conditional of β ?