This paper describes a general scheme for accomodating different types of conditional distributions in a Bayesian network. The algorithm is based on the polytree algorithm for Bayesian network inference, in which “messages” (probability distributions and likelihood functions) are computed. The posterior for a given variable depends on the messages sent to it by its parents and children, if any....

Network attack graphs are originally used to evaluate what the worst security state is when a concerned network is under attack. Combined with intrusion evidence such like IDS alerts, attack graphs can be further used to perform security state posterior inference (i.e. inference based on observation experience). In this area, Bayesian network is an ideal mathematic tool, however it can not be d...

MOTIVATION Bayesian methods are widely used in many different areas of research. Recently, it has become a very popular tool for biological network reconstruction, due to its ability to handle noisy data. Even though there are many software packages allowing for Bayesian network reconstruction, only few of them are freely available to researchers. Moreover, they usually require at least basic p...

In this thesis, we provide a general background for inference and learning, using Bayesian networks and genetic algorithms. We introduce Bayesian Networks in Java, a Java-based Bayesian network API that we have developed. We describe our research with structure learning using a genetic algorithm to search the space of adjacency matrices for a Bayesian network. We first instantiate the populatio...

In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classiier selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby learning Bayesian networks with a bias for small, high-predictive-accuracy networks. We compare the performance of this classiier with selective and non-selective naive...

This paper presents a floating search approach for learning the network structure of Bayesian network classifiers. A Bayesian network classifier is used which in combination with the search algorithm allows simultaneous feature selection and determination of the structure of the classifier. The introduced search algorithm enables conditional exclusions of previously added attributes and/or arcs...

Various algorithms have been developed for variant calling using next-generation sequencing data, and various methods have been applied to reduce the associated false positive and false negative rates. Few variant calling programs, however, utilize the pedigree information when the family-based sequencing data are available. Here, we present a program, FamSeq, which reduces both false positive ...

many efforts have been made to model the the hot deformation (dynamic recrystallization) flow curves of different materials. phenomenological constitutive models, physical-based constitutive models and artificial neural network (ann) models are the main methods used for this purpose. however, there is no report on the modeling of warm deformation (dynamic spheroidization) flow curves of any kin...

We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a variational Bayesian structural expect...

This paper formulates learning optimal Bayesian network as a shortest path finding problem. An A* search algorithm is introduced to solve the problem. With the guidance of a consistent heuristic, the algorithm learns an optimal Bayesian network by only searching the most promising parts of the solution space. Empirical results show that the A* search algorithm significantly improves the time an...