A0716
Title: Approximations in the Ising model for use in scene analysis
Authors: Alejandro Murua - University of Montreal (Canada) [presenting]
Ranjan Maitra - Iowa State University (United States)
Abstract: The Ising distribution is useful in many applications involving statistical modelling and inference. However, its normalization is constant, and its moments are intractable. This drawback has prevented a wider use of this model. Analytical approximations are provided that make it possible to estimate these quantities numerically in a homogeneous case. Simulation studies indicate good performance compared to Markov chain Monte Carlo methods and in a tiny fraction of the time. The methodology is illustrated in performing Bayesian inference in a functional magnetic resonance imaging activation detection experiment.
