Fully Bayesian binary Markov random field models: Prior specification and posterior simulation

TL;DR

This paper introduces a flexible Bayesian prior model for binary Markov random fields, allowing higher-order interactions and adaptive complexity, along with a reversible jump MCMC algorithm for posterior sampling, demonstrated on simulated and real data.

Contribution

It develops a novel prior model for binary MRFs that accommodates higher-order interactions and adaptive parameter complexity, with an efficient sampling algorithm.

Findings

Flexible prior model effectively captures complex structures.

Reversible jump MCMC successfully samples from intractable posteriors.

Demonstrated on both simulated and real datasets.

Abstract

We propose a flexible prior model for the parameters of binary Markov random fields (MRF) defined on rectangular lattices and with maximal cliques defined from a template maximal clique. The prior model allows higher-order interactions to be included. We also define a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to sample from the associated posterior distribution. The number of possible parameters for an MRF with for instance k x l maximal cliques becomes high even for small values of k and l. To get a flexible model which may adapt to the structure of a particular observed image we do not put any absolute restrictions on the parametrisation. Instead we define a parametric form for the MRF where the parameters have interpretation as potentials for the various clique configurations, and limit the effective number of parameters by assigning apriori discrete probabilities for events where groups of parameter values are equal. To run our RJMCMC algorithm we have to cope with the computationally intractable normalising constant of MRFs. For this we adopt a previously defined approximation for binary MRFs, but we also briefly discuss other alternatives. We demonstrate the flexibility of our prior formulation with simulated and real data examples.