I'm working with Bayesian hierarchichal regressions fitted with R-INLA. I would like to simplify my model by reducing the number of covariates.
According to my understanding, Bayesian variable selection (spike & slab priors) cannot be done with R-INLA . I don't like the idea of forward/backward selection based on some information criteria (WAIC, DIC).
What approaches would you recommend for variable selection in this context? I'd appreciate it if you could cite your sources.