A Distributed Sensor Network (DSN) consists of a set of sensors that are interconnected by a communication network. DSN is capable of acquiring and processing signals, communicating, and performing simple computational tasks. Such sensors can detect and collect data concerning any sign of node failure, earthquakes, floods and even a terrorist attack. Energy efficiency and fault-tolerance networ...

Recent developments in high-throughput technologies have been very helpful towards understanding the molecular abnormalities observed in disease conditions such as cancer. High-throughput experiments, which provide transcriptome-wide expression information for a cell give better insight into understanding the differences of biological processes between normal and pathological conditions. Instea...

This paper enlightens Bayesian Networks (BNs) potentialities as a support tool, thanks to their capability of providing a graphic and intuitive representation of any process. As an engineering tool, BNs are sometimes used for reliability evaluation and in maintenance management of complex systems, but, as a matter of fact, they could be applied nearly to any field. This paper aims at illustrati...

The main goal of this paper is to describe inference in hybrid Bayesian networks (BNs) using mixtures of polynomials (MOP) approximations of probability density functions (PDFs). Hybrid BNs contain a mix of discrete, continuous, and conditionally deterministic random variables. The conditionals for continuous variables are typically described by conditional PDFs. A major hurdle in making infere...

Bayesian networks (BNs) offer a compact, intuitive, and efficient graphical representation of uncertain relationships among the variables in a domain and have proven their value in many disciplines over the last two decades. However, two challenges become increasingly critical in practical applications of Bayesian networks. First, real models are reaching the size of hundreds or even thousands ...

Learning the conditional probability table (CPT) parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are limited data available. A conventional way to address this challenge is to introduce domain knowledge/expert judgments that are encoded as qualitative parameter constraints. In this paper we focus on a class of constrain...

Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) are becoming more widely used as a way to learn various types of networks, including cellular signaling networks, from high-throughput data. Due to the high cost of performing experiments, we are interested in developing an experimental design for time series data generation. Specifically, we are interested in determining properties o...

Legal reasoning with evidence can be a challenging task. We study the relation between two formal approaches that can aid the construction of legal proof: argumentation and Bayesian networks (BNs). Argument schemes are used to describe recurring patterns in argumentation. Critical questions for many argument schemes have been identified. Due to the increased use of statistical forensic evidence...

In this paper, we empirically evaluate algorithms for learning four Bayesian network (BN) classifiers: Naïve-Bayes, tree augmented Naïve-Bayes (TANs), BN augmented NaïveBayes (BANs) and general BNs (GBNs), where the GBNs and BANs are learned using two variants of a conditional independence based BN-learning algorithm. Experimental results show the GBNs and BANs learned using the proposing learn...

The problem of learning discrete Bayesian networks from data is encoded as a weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to address it. For each dataset, the per-variable summands of the (BDeu) marginal likelihood for different choices of parents (‘family scores’) are computed prior to applying MaxWalkSat. Each permissible choice of parents for each variable is en...