Learning of classical lattice Hamiltonians
We address the problem of learning of classical Markov Random Fields that are widely used in material science, statistical physics, and computer science to represent structured Gibbs distributions. We introduce a new computationally efficient Interaction Screening method for learning discrete and continuous Gibbs distributions for which maximum likelihood approaches are intractable. The algorithm recovers the structure and parameters of the Hamiltonians with multi-body interactions specified in an arbitrary basis.
