Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take place are treestructures, and fixed-orderings with limited in-degree. We show how...

Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take place are treestructures, and fixed-orderings with limited in-degree. We show how...

Although the last forty years has seen considerable growth in the use of statistics in legal proceedings, it is primarily classical statistical methods rather than Bayesian methods that have been used. Yet the Bayesian approach avoids many of the problems of classical statistics and is also well suited to a broader range of problems. This paper reviews the potential and actual use of Bayes in t...

Mixture models for random graphs have a complex dependency structure and a likelihood which is not computable even for moderate size networks. Variational and variational Bayes techniques are useful approaches for statistical inference of such complex models but their theoretical properties are not well known. We give a result about the consistency of variational estimates of the parameters of ...

We have already proposed a constraint-based learning Bayesian network method using Bayes factor. Since a conditional independence test using Bayes factor has consistency, the learning method improves the learning accuracy of the traditional constraint-based learning methods. Additionally, the method is expected to learn larger network structures than the traditional methods do because it greatl...

We present an algorithm for learning correlations among link types and node attributes in relational data that represent complex heterogeneous networks. The link correlations are represented in a Bayes net structure. The current state of the art algorithm for learning relational Bayes nets captures only correlations among entity attributes given the existence of links among entities. The models...

We describe the Scyllarus system, which performs Intrusion Detection System (IDS) fusion, using Bayes nets and qualitative probability.1 IDSes are systems that sense intrusions in computer networks and hosts. IDS fusion is the problem of fusing reports from multiple IDSes scattered around a computer network we wish to defend, into a coherent overall picture of network status. Scyllarus treats t...

The focus of the modeling framework is on interactions between human and automation agents in large, distributed agent networks/systems. This model combines Bayes nets with Game Theoretic methods to model human behavior and predict the behavior of a composite system involving humans and automation. In general, some of the nodes of the Bayes net will be set by the humans in the system, some will...

In this paper, several neural network and statistical learning approaches are proposed that learn to make human like decisions for the job assignment problem of the US Navy. Comparison study of Feedforward Neural Networks (FFNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Adaptive Bayes (AB) classifier with Generalized Estimation Equation (GEE) is provided. ...

Selecting a minimum set of core features for automatic network intrusion detection with a variety of machine learning algorithms is a challenging problem. In this paper we propose a minimum feature set which can be easily extracted from network traffic. We compare decision trees, neural networks, naive Bayes and Bayesian networks classifiers performing on the KDDCup99 datasets. We show that by ...