Markov Random Fields are widely used in many image processing applications. Recently the shortcomings of some of the simpler forms of these models have become apparent, and models ...
Abstract. We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n ≥ 4) binary variables X1, . . . , Xn. Our inference pri...
We propose a model for a point-referenced spatially correlated ordered categorical response and methodology for estimation of model parameters. Models and methods for spatially co...
We present a Bayesian clustering algorithm for multivariate time series. A clustering is regarded as a probabilistic model in which the unknown auto-correlation structure of a tim...
Mark-recapture models have for many years been used to estimate the unknown sizes of animal and bird populations. In this article we adapt a finite mixture mark-recapture model i...