Revisiting Boltzmann learning: parameter estimation in Markov random fields

This contribution concerns a generalization of the Boltzmann Machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization and generalization in the context of Boltzmann Machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov Field.