In this paper we propose the use of Bayesian Networks as a theoretical framework for Computerized Adaptive Tests. To this end, we develop the Bayesian Network that supports the Adaptive Testing Algorithm, that is, we define what variables should be taken into account, what kind of relationships should be established among them, and what are the required parameters. As parameter specification is...

In this paper, we define a new information theoretic measure that we call the “uprooted information”. We show that a necessary and sufficient condition for a probability P (s|do(t)) to be “identifiable” (in the sense of Pearl) in a graph G is that its uprooted information be non-negative for all models of the graph G. In this paper, we also give a new algorithm for deciding, for a Bayesian net ...

Context-specific independence (CSI) refers to conditional independencies that are true only in specific contexts. It has been found useful in various inference algorithms for Bayesian networks. This paper studies the role of CSI in general. We provide a characterization of the computational leverages offered by CSI without referring to particular inference algorithms. We identify the issues tha...

We present a simple characterization of equivalent Bayesian network structures based on local transformations. The sig­ nificance of the characterization is twofold. First, we are able to easily prove several new invariant properties of theoretical in­ terest for equivalent structures. Second, we use the characterization to derive an ef­ ficient algorithm that identifies all of the compelled ed...

The arc reversal/node reduction approach to probabilistic inference is extended to include the case of instantiated evidence by an operation called "evidence reversal." This not only provides a technique for computing posterior joint distributions on general belief networks, but also provides insight into the methods of Pearl { 1986a] and Lauritzen and Spiegelhalter {1988]. Although it is well ...

Certain class es of problems, including per­ ceptual data understanding, robotics, discov­ ery, and learning, can be represented as incre­ mental, dynamically constructed belief net­ works. These automatically constructed net­ works can be dynamically extended and mod­ ified as evidence of new individuals becomes available. The main result of this paper is the incremental extension of the singl...

The application of a radial basis function network to digital communications channel equalization is examined. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equaliza...