Håvard Rue

Approximate Bayesian inference for latent MRF and Gaussian models

In this talk I will discuss some techniques for doing approximate
Bayesian inference for latent MRF and Gaussian models. The task is to
approximate the posterior marginals for the hyperparameters and the
latent field. For the MRF case, we build our approximations making
use of exact results for small lattices (smallest dimension less than
20). For the latent Gaussian case, we use integrated nested Laplace
approximations. The approximations are very precise and (relatively)
quick to compute, and indicate that inference based on Markov chain
Monte Carlo for such models is not needed.