Submitted to the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition Maximum Likelihood Estimation of Markovian Prior Parameters Using Markov Chain Monte Carlo

Recent developments in statistics now allow maximum likelihood estimators for the parameters of Markov Random Fields to be constructed. We detail the theory required, and present an algorithm which is easily implemented and practical in terms of computation time. We demonstrate this algorithm on three MRF models, the standard Potts model, an inhomogeneous variation of the Potts model and a long-range interaction model, better adapted to modelling real-world images. We estimate the parameters from a synthetic and a real image, and then resynthesise the models to demonstrate which features of the image have been captured by the model.