  Fitting probabilistic models to data is often difficult, due to the general
intractability of the partition function. We propose a new parameter fitting
method, Minimum Probability Flow (MPF), which is applicable to any parametric
model. We demonstrate parameter estimation using MPF in two cases: a continuous
state space model, and an Ising spin glass. In the latter case it outperforms
current techniques by at least an order of magnitude in convergence time with
lower error in the recovered coupling parameters.
