On the estimation of Markov random field parameters
We examine the histogram method proposed in [1] for estimating the parameters associated with a Markov random field. This method relies on the estimation of the local interaction sums from histogram data. We derive an estimator for these quantities that is optimal in a well-defined sense. Furthermore. we show that the final step of the histogram method, the solution of a least-squares problem. can be done substantially faster than one might expect it no equation culling is used. We also examine the use of weighted least-squares and see that this seems to lead to better estimates even with smalt amounts of data.
markov random fields parameter estimation