Probability theory provides the theoretical basis for a logically coherent process of combining prior knowledge with empirical data to draw plausible inferences and to refine theories as observations accrue. Increases in the expressive power of languages for expressing probabilistic theories have been accompanied by refinements and adaptations of Bayesian learning methods to handle the more exp...

Multi-classifier combination based on Dempster-Shafer theory of evidence has demonstrated it’s superior performance. In the approach based on Dempster-Shafer theory, the basic probability assignments for evidence are usually derived from classifiers’ global performance. However, our study discovered that while using classifiers’ global performance as basic probability assignments doesn’t necess...

This report updates the status of the Landing Craft Air Cushion Vehicle (LCAC) Navigator Selection System prediction algorithm. The last revision took place at the end of 1997. With the receipt of new training data, the prediction algorithm was changed in June 2000 to take advantage of these new data. Some rough estimates of the new attrition rate and the rejection rate due to the selection sys...

Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also consistent and eecient. We provide a brief overview of MML inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)), and how it has both an information-theoretic and a Bayesian interpretation. We then outline how MML is used for statistical parameter estimation, and how the MML mix...

The problem of density estimation occurs in many disciplines. For example, in MRI it is often necessary to classify the types of tissues in an image. To perform this classification one must first identify the characteristics of the tissues to be classified. These characteristics might be the intensity of a T1 weighted image and in MRI many other types of characteristic weightings (classifiers) ...

Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also consistent and eecient. We provide a brief overview of MML inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)), and how it has both an information-theoretic and a Bayesian interpretation. We then outline how MML is used for statistical parameter estimation, and how the MML mix...

Quasi-Bayesian theory uses convex sets of probability distributions and expected loss to represent preferences about plans. The the­ ory focuses on decision robustness, i .e., the extent to which plans are affected by devi­ ations in subjective assessments of probabil­ ity. Generating a plan means enumerating the actions to be taken and providing infor­ mation about the robustness of the action...