📝 SummarXiv

Abstract

The Ising and Potts models, among the most important models in statistical physics, have been used for modeling binary and multinomial data on lattices in a wide variety of disciplines such as psychology, image analysis, biology, and forestry. However, these models have several well known shortcomings: (i) they can result in poorly fitting models, that is, simulations from fitted models often do not produce realizations that look like the observed data; (ii) phase transitions and the presence of ground states introduce significant challenges for statistical inference, model interpretation, and goodness of fit; (iii) intractable normalizing constants that are functions of the model parameters pose serious computational problems for likelihood-based inference. Here we develop a tapered version of the Ising and Potts models that addresses issues (i) and (ii). We develop efficient Markov Chain Monte Carlo Maximum Likelihood Estimation (MCMCMLE) algorithms that address issue (iii). We perform an extensive simulation study for the classical and Tapered Potts models that provide insights regarding the issues generated by the phase transition and ground states. Finally, we offer practical recommendations for modeling and computation based on applications of our approach to simulated data as well as data from the 2021 National Land Cover Database.