As the combination of parameter learning and structure learning, learning Bayesian networks can also be examined, Parameter learning is estimation of the dependencies in the network. Structural learning is the estimation of the links of the network. In terms of whether the structure of the network is known and whether the variables are all observable, there are four types of learning Bayesian n...

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. Dynamic Bayesian networks are an extension of Bayesian networks for modeling dynamic processes. In this paper we present a decision support system based on a dynamic Bayesian network. Its purpose is to monitor the dry weight of patients suffering from chronic renal failure treated by he...

We give an overview of a minimal set of tools for explanation in decision problems formulated as Bayesian networks. After an introduction to the Bayesian network paradigm we introduce sensitivity analysis and data conflict analysis as applied to Bayesian networks. Finally, we apply these tools to BOBLO, a large Bayesian network for determining the blood group of Jersey cattle.

This paper presents an intelligent user interface agent architecture based on Bayesian networks. Using a Bayesian network knowledge representation not only dynamically captures and models user behavior, but it also dynamically captures and models uncertainty in the interface’s reasoning process. Bayesian networks’ sound semantics and mathematical basis enhances it’s ability to make correct, int...

A difficult task in modeling with Bayesian networks is the elicitation of numerical parameters of Bayesian networks. A large number of parameters is needed to specify a conditional probability table (CPT) that has a larger parent set. In this paper we show that, most CPTs from real applications of Bayesian networks can actually be very well approximated by tables that require substantially less...

Introduction In modeling real world tasks, one inevitably has to deal with uncertainty. This uncertainty is due to the fact that many facts are unknown and or simply ignored and summarized. Suppose that one morning you find out that your grass is wet. Is it due to rain, or is it due to the sprinkler? If there is no other information, you can only talk in terms of probabilities. In a probabilist...

During the last several decades, the Bayesian networks have turned into a dynamic area of research. This great interest is owning to the advantages offered by special structure of Bayesian networks, which allows them to be very efficient in modeling domains with inherent uncertainty. Bayesian networks techniques can be successfully applied to mining various types of biomedical data. This chapte...

This article demonstrates how Bayesian networks can be employed as a tool to assess the quality of care in nursing homes. For the data sets analyzed, the proposed model performs comparably to existing quantitative assessment models. In addition, a Bayesian network approach offers several unique advantages. The structure and parameters of a Bayesian network provide rich insight into the multidim...

In this paper we describe how to learn Bayesian networks from a summary of complete data in the form of a dependency network rather than from data directly. This method allows us to gain the advantages of both representations: scalable algorithms for learning dependency networks and convenient inference with Bayesian networks. Our approach is to use a dependency network as an “oracle” for the s...