Bayesian graphical model determination using decision theory

Bayesian Decision Making Theory

Inferring regulatory networks using a hierarchical Bayesian graphical Gaussian model

In this paper, we propose a new formalism based on graphical Gaussian model (GGM) to infer genetic regulatory networks. A hierarchical Bayesian prior for the precision matrix of the GGM is introduced to impose a bias toward sparse graph structure. We show that the MAP estimation of the undirected graph can be readily obtained by a variant of the well-known Lasso regression algorithm. Then we in...

A Bayesian model decision support system: dryland salinity management application

Addressing environmental management problems at catchment scales requires an integrated modelling approach, in which key bio-physical and socio-economic drivers, processes and impacts are all considered. Development of Decision Support Systems (DSSs) for environmental management is rapidly progressing. This paper describes the integration of physical, ecological, and socio-economic components i...

Bayesian Decision Theory and Psychophysics

We argue that Bayesian decision theory provides a good theoretical framework for visual perception. Such a theory involves a likelihood function specifying how the scene generates the image(s), a prior assumption about the scene, and a decision rule to determine the scene interpretation. This is illustrated by describing Bayesian theories for individual visual cues and showing that perceptual b...

Diagnostic testing: Model estimation and decision support using graphical models

Many agricultural decision problems can be treated within the framework of diagnostic testing. Observations are made sequentially in to order to classify a production unit as normal/unnormal, diseased/healthy etc. The decision problem is to find the optimal classification depending on the observations. The framework is well established and optimal use of such diagnostic testing schemes require ...