Optimization and inference are two important computational problems that arise in many machine learning and physical contexts. Bayesian inference consists of the computation of marginal probabilities in high dimensional probability models. It is at the core of many machine learning applications such as computer vision, robotics, expert systems and pattern recognition. Also optimization is found in many applications such as optimal control, Markov decision processes and expert systems.