Before we discuss the details of the Bayesian detection, let us take a quick tour about the overall framework to detect (or classify) an object in practice. In the Bayesian setting, we model observations as random samples drawn from some probability distributions. The classification process usually involves extracting features from the observations, and a decision rule that satisfies certain op...

In this contribution we describe an object{oriented software architecture for image segmentation, 3{D pose estimation as well as Bayesian object recognition: models are represented by densities, model generation corresponds to parameter estimation tasks, and the identi cation applies the Bayesian decision rule. We show results of 3{D object recognition experiments based on the observation of 2{...

Research into Bayesian analysis of computer simulations has appeared mostly in the simulation literature. Very little of the work has been performed by decision analysts, despite the overlap of the methods used. The hope is that this expository survey on Bayesian simulation will engender more work in the area by decision analysts. We discuss the main areas of research performed thus far, includ...

A dynamic decision model can facilitate the complicated decision-making process in medicine, in which both time and uncertainty are explicitly considered. In this paper, we address the problem of automatic construction of a dynamic decision model from a large medical database. Within the DynaMoL (a dynamic decision modeling language) framework, a model can be represented in influence view. Thus...

This paper re-visits the Bayesian approach to test its efficacy in optimally designing the classical (s, Q) inventory model. A heuristic search of the unstructured (s, Q) decision space finds that one can indeed make a decent start by the Bayesian approach—and keep total cost nearly optimally low throughout.

A Bayesian network is a powerful graphical model. It is advantageous for real-world data analysis and finding relations among variables. Knowledge presentation and rule generation, based on a Bayesian approach, have been studied and reported in many research papers across various fields. Since a Bayesian network has both causal and probabilistic semantics, it is regarded as an ideal representat...

We describe a computational model of motor areas of the cerebral cortex. The model combines Bayesian networks, competitive learning and reinforcement learning. We found that decision-making using MPE (Most Probable Explanation) approximates the ideal decisionmaking in this model, which suggests that MPE calculation is a promising model of not only sensory-cortex recognition, already addressed b...

In this paper, a novel approach of developing the Bayesian network for fault diagnosis based on operation procedures is presented. The proposed Bayesian network consists of operation procedure layer, fault layer and fault symptom layer. First, operation procedure layer containing procedure nodes and state decision nodes is developed. Second, the fault layer is determined based on the state deci...