We consider a logistic regression. The spatial dependence is captured through a hidden Gaussian process after
the logit transformation of the Bernoulli success probabilities. In a hierarchical framework, likelihood-based
estimation requires an EM algorithm. However, the expectations in the E-step are not available in closed-from
expressions. We propose a variational approximation of the complete likelihood, that has a Gaussian form. We then
obtain the desired approximations of the expectations. We conduct a simulation study to compare our approach
with Laplace approximation.
