In this paper, we are trying to examine trade offs between fuzzy logic and certain Bayesian networks and we propose to combine their respective advantages into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of fuzzy random variables. This paper deals with different definitions and classifications of uncertainty, sources of uncertainty, and theories and methodologies present...

This paper presents new methods for generation of random Bayesian networks. Such methods can be used to test inference and learning algorithms for Bayesian networks, and to obtain insights on average properties of such networks. Any method that generates Bayesian networks must first generate directed acyclic graphs (the “structure” of the network) and then, for the generated graph, conditional ...

This paper introduces a novel enhancement for learning Bayesian networks with a bias for small, high-predictive-accuracy networks. The new approach selects a subset of features that maximizes predictive accuracy prior to the network learning phase. We examine explicitly the eeects of two aspects of the algorithm, feature selection and node ordering. Our approach generates networks that are comp...

Deceptive and other functions of unitation have been considered in order to understand which ¿tness functions are hard and which are easy for genetic algorithms to optimize. This paper focuses on genetic algorithm ¿tness functions represented as Bayesian networks. We investigate onemax, trap, and hill functions of unitation when converted into Bayesian networks. Among other things, this paper s...

Object-oriented paradigms have been applied to Bayesian networks to provide a modular structure which allows greater flexibility and robustness. These object-oriented Bayesian networks may be used over larger and more complex domains. However, as the networks get larger, the computational cost of triangulation and junction tree construction grows. The process of creating new junction trees when...

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we pro...

High-throughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules (for example, in the analysis of intracellular multicolor flow cytometry). We d...

This paper illustrates a new criterion for evaluating neural networks stability compared to the Bayesian classifier. The stability comparison is performed by the error rate probability densities estimation using the modified semi-bounded Plug-in algorithm. We attempt, in this work, to demonstrate that the Bayesian approach for neural networks improves the performance and stability degree of the...

Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in the face of extremely unlikely evidence. In addressing this problem, importance sampling algorithms seem to be most successful. We discuss the principles underlying the importance sampling algorithms in Bayesian networks. After that, we describe Evidence Pre-propagation Importance Sampling (EPIS...

Several researchers have suggested that Bayesian networks be used in web search and user profiling. One advantage of this approach is that Bayesian networks are more general than the probabilistic models previously used in information retrieval. In practice, experimental results demonstrate the effectiveness the modern Bayesian network approach. On the other hand, since Bayesian networks are de...