We introduce a flexible and scalable class of Bayesian geostatistical models for discrete data, based on nearest-neighbor mixture processes (NNMP), referred to as NNMP. To define the joint probability mass function (pmf) over set spatial locations, we build from local mixtures conditional pmfs using directed graphical model, with acyclic graph that summarizes nearest neighbor structure. The app...

In previous work 6, 9, 10], we advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity based primarily on a Bayesian (MAP) analysis of image diier-ences, leading to a \dual" basis similar to eigenfaces 13]. The performance advantage of this probabilistic matching technique over standard E...

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Abstract Structural equation models (SEMs) with latent variables are routinely used in social science research, and are of increasing importance in biomedical applications. Standard practice in implementing ...

This paper investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection. We introduce a Bayesian approach that models and detects both (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively n...

This paper investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (...