In this paper, a popular metapopulation model is critically examined by putting the model in the context of Markov random fields and the statistical analysis of binary lattice systems. The claim that the model can be used to estimate time-process parameters from spatial-pattern data is examined on a real data set where process information was available.

In this paper we propose a non-Gibbsian Markov random field to model the spatial and topological relationships between objects in structured scenes. The field is formulated in terms of conditional probabilities learned from a set of training images. A locally consistent labelling of new scenes is achieved by relaxing the Markov random field directly using these conditional probabilities. We eva...

This paper deals with the statistical segmentation of multisensor images. In a Bayesian context, the interest of using hidden Markov random fields, which allows one to take contextual information into account, has been well known for about 20 years. In other situations, the Bayesian framework is insufficient and one must make use of the theory of evidence. The aim of our work is to propose evid...

In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and ob...

In this project1, we study the hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm. We implement a MATLAB toolbox named HMRF-EM-image for 2D image segmentation using the HMRF-EM framework2. This toolbox also implements edge-prior-preserving image segmentation, and can be easily reconfigured for other problems, such as 3D image segmentation.

1. Markov property The Markov property of a stochastic sequence {Xn}n≥0 implies that for all n ≥ 1, Xn is independent of (Xk : k / ∈ {n− 1, n, n + 1}), given (Xn−1, Xn+1). Another way to write this is: Xn ⊥ (Xk : k / ∈ ∂{n}) | (Xk : k ∈ ∂{n}) where ∂{n} is the set of neighbors of site n. We would like to now generalize this Markov property from one-dimensional index sets to more arbitrary domains.

in recent years, some statisticians have studied the signal detection problem by using the random field theory. in this paper we have considered point estimation of the gaussian scale space random field parameters in the bayesian approach. since the posterior distribution for the parameters of interest dose not have a closed form, we introduce the markov chain monte carlo (mcmc) algorithm to ap...