Spreading Stochastic Models Under Ising/Potts Random Fields

Abstract

Stochastic models have become a powerful necessary statistical tool to estimate parameters where data are represented by regions of interests under uncertain areas. Due to the high dimensionality of the spatial patterns, investigation of the stochastic modeling simulations must be applied based on spatial variability. Models for spreading diseases are given based on whether or not the disease succeeds or fails to appear in the region. Based on this assumption, an Ising/Potts random fields model has to be introduced to analyze the spatial pattern of spreading. In this work, the spatial pattern models for spreading diseases have been analyzed considering Markov random fields auto-models. The Gibbs sampler would be used to simulate example images for various parameter combinations. In this work, a spatial analysis methodology based on Bayesian analysis was introduced, and procedures to solve the problem with spatial variability are described based on spatial model estimation techniques.