Ensuring the availability of enterprise IT systems is a challenging task. The factors that can bring systems down are numerous, and their impact on various system architectures is difficult to predict. At the same time, maintaining high availability is crucial in many applications, ranging from control systems in the electric power grid, over electronic trading systems on the stock market to sp...

We utilize the discriminant analysis to select wavelet features for efficient object detection. The analysis applies to the Bayesian classifier and is extended to the case of boosting. Based on the error analysis under the Bayesian decision rule, we reduce the number of coefficients involved in detection to lower the computational cost. Using a Hidden Markov Tree (HMT) model to describe the pat...

Sequential Bayesian information fusion is a process in which streams of observations from multiple sources are fused in order to form a more complete and accurate situation assessment. Allthough the information sources are potentially unreliable, and we generally have little control over them, it is possible to directly influence the accuracy of the fusion through the parameters of the Bayesian...

Bayesian Network has provided a convenient frame structure to express causal relationship, by regarding influence diagram as a special Bayesian Network, then the value of each decision variable is imposed externally to meet the goals, rather than derive from determination probability of father node. Using influence diagram to describe the node types of directed acyclic graph has been studied, w...

In this paper I present an axiomatic choice theory for Bayesian decision makers. I use this model to define choice-based subjective probabilities that truly represent Bayesian decision makers’ prior and posterior beliefs. I argue that because of the limitations of the traditional analytical framework, no equivalent results may be obtained for theories that invoke Savage’s (1954) idea of a state...

I consider decision makers who experience framing effects facing different choice problems, such that the resulting random choice is incompatible with a single random utility model. I study conditions under which these framing effects could be exhibited by a population of expected utility maximizing (“Bayesian”) agents uncertain about their own tastes. The model assumes that framing of a choice...

Intrusion detection systems (IDSs) have become a critical part of security systems. The goal of an intrusion detection system is to identify intrusion effectively and accurately. However, the performance of misuse intrusion detection system (MIDS) or anomaly intrusion detection system (AIDS) is not satisfying. In this paper, we study the issue of building a compound intrusion detection model, w...

Quantifying parameter and prediction uncertainty in a rigorous framework can be an important component of model skill assessment. Generally, models with lower uncertainty will be more useful for prediction and inference than models with higher uncertainty. Ensemble estimation, an idea with deep roots in the Bayesian literature, can be useful to reduce model uncertainty. It is based on the idea ...

Bayesian learning methods have recently been shown to provide an elegant solution to the exploration-exploitation trade-off in reinforcement learning. However most investigations of Bayesian reinforcement learning to date focus on the standard Markov Decision Processes (MDPs). The primary focus of this paper is to extend these ideas to the case of partially observable domains, by introducing th...

The decision-theoretic rough set model based on Bayesian decision theory is a main development tendency in the research of rough sets. To extend the theory of decision-theoretic rough set, the article devotes this study to presenting multigranulation decision-theoretic rough set model in ordered information systems. This new multigranulation decision-theoretic rough set approach is characterize...