In this article a new probabilistic information retrieval (IR) model, based on Bayesian networks (BNs), is proposed. We first consider a basic model, which represents only direct relationships between the documents in the collection and the terms or keywords used to index them. Next, we study two versions of an extended model, which also represents direct relationships between documents. In eit...

One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possible solutions and turned out to be a wellknown NP -hard problem and, hence, approximations are required. However, to the best of our knowledge, a quantitative analysis of the performance and characteristics of the dif...

This paper studies the architectural problem of branch prediction. We analyse the popular technique of two-level adaptive prediction, relating it to the state-of-the-art Machine Learning technique of Bayesian Networks (BNs). We show that a two-level predictor is an approximation to the BN formalism. This link allows us to explore the wider family of BN predictors. We investigate how to adapt BN...

Recent developments in the forensic sciences have confronted the field of legal reasoning with the new challenge of reasoning under uncertainty. Forensic results come with uncertainty and are described in terms of likelihood ratios and random match probabilities. The legal field is unfamiliar with numerical valuations of evidence, which has led to confusion and in some cases to serious miscarri...

This report describes the use of Bayesian networks (BNs) to model statistical loss distributions in financial operational risk scenarios. Its focus is on modeling "long" tail, or unexpected, loss events using mixtures of appropriate loss frequency and severity distributions where these mixtures are conditioned on causal variables that model the capability or effectiveness of the underlying cont...

This report describes the use of Bayesian Networks (BNs) to model statistical loss distributions in financial operational risk scenarios. Its’ focus is on modelling “thick” tail, or unexpected, loss events using mixtures of appropriate loss frequency and severity distributions where these mixtures are conditioned on causal variables that model the capability or effectiveness of the underlying c...

Bayesian networks (BNs) were pioneered to solve problems in Artificial Intelligence (AI) and have proven successful in “intelligent” applications such as medical expert systems, speech recognition, and fault diagnosis. In practical terms, one of the major benefits from using BNs is in that probabilistic and causal relationships among variables are represented and executed as graphs and can thus...

Bayesian network (BN) models gain more and more popularity as a tool in reliability analysis. In this paper we consider some of the properties of BNs that have made them popular, consider some of the recent developments, and also point to the most important remaining challenges when using BNs in reliability.

Bayesian networks (BNs) are used for inference and sampling by exploiting conditional independence among random variables. Context specific independence (CSI) is a property of graphical models where additional independence relations arise in the context of particular values of random variables (RVs). Identifying and exploiting CSI properties can simplify inference. Some generative network model...

Non-impeding noisy-And Trees (NATs) provide a general, expressive, and efficient causal model for conditional probability tables (CPTs) in discrete Bayesian networks (BNs). A BN CPT may either be directly expressed as a NAT model or be compressed into one. Once CPTs in BNs are so expressed or compressed, complexity of inference (both space and time) can be significantly reduced. The most import...