In order to employ machine learning in realistic clinical settings we are in need of algorithms which show robust performance, producing results that are intelligible to the physician. In this article, we present a new Bayesian-network learning algorithm which can be deployed as a tool for learning Bayesian networks, aimed at supporting the processes of prognosis or diagnosis. It is based on a ...

Abstract: This paper proposes a novel scheme for multi-static passive radar processing, based on soft-input soft-output processing and Bayesian sparse estimation. In this scheme, each receiver estimates the probability of target presence based on its received signal and the prior information received from a central processor. The resulting posterior target probabilities are transmitted to the c...

This paper describes a parallel version of the PC algorithm for learning the structure of a Bayesian network from data. The PC algorithm is a constraint-based algorithm consisting of five steps where the first step is to perform a set of (conditional) independence tests while the remaining four steps relate to identifying the structure of the Bayesian network using the results of the (condition...

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...

Various different algorithms for learning Bayesian networks from data have been proposed to date. In this paper, we adopt a novel approach that combines the main advantages of these algorithms yet avoids their difficulties. In our approach, first an undirected graph, termed the skeleton, is constructed from the data, using zeroand first-order dependence tests. Then, a search algorithm is employ...

Network security attacks are the violation of information security policy that received much attention to the computational intelligence society in the last decades. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naïve Bayesian classifier is one of the most popular data mining algorithm fo...

Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. Inference algorithms for specialized classes of belief networks have been shown to be more efficient. In this paper, we present a searchbased algorithm for approximate inference on arbitrary, noisy-OR belief networks, generalizing earlier work on search-based inference for twolevel, noisy-OR b...

The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with constraint networks. We define the semantics and graphical representation, outline the primary algorithms for processing mixed networks and provide some empirical demonstration.

In order to make an informed decision in a criminal trial, conclusions about what may have happened need to be derived from the available evidence. Recently, Bayesian networks have gained popularity as a probabilistic tool for reasoning with evidence. However, in order to make sense of a conclusion drawn from a Bayesian network, a juror needs to understand the context. In this paper, we propose...