The proliferation of smartphones has lead to the development of a large variety of applications and the inverstigation on the use of various sensors through contextawareness, in order to provide better services. However, smartphone battery capacity is extremely limited, so that the applications cannot be effectively used. In this paper, we propose a low-power context-aware system using modular ...

-Possibilistic logic and Bayesian networks have provided advantageous methodologies and techniques for computerbased knowledge representation. This paper proposes a framework that combines these two disciplines to exploit their own advantages in uncertain and imprecise knowledge representation problems. The framework proposed is a possibilistic logic based one in which Bayesian nodes and their ...

There are several formalisms that enhance Bayesian networks by including relations amongst individuals as modeling primitives. For instance, Probabilistic Relational Models (PRMs) use diagrams and relational databases to represent repetitive Bayesian networks, while Relational Bayesian Networks (RBNs) employ first-order probability formulas with the same purpose. We examine the coherence checki...

This paper deals with the use of Bayesian networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian networks are identified. The basic concepts of the Bayesian networks application to reliability analysis are introduced and a ...

One of the fundamental issues of Bayesian networks is their representational power, re-ecting what kind of functions they can or cannot represent. In this paper, we rst prove an upper bound on the representational power of Augmented Naive Bayes. We then extend the result to general Bayesian networks. Roughly speaking, if a function contains an m-XOR, there exists no Bayesian networks with node ...

The Markov library contains methods to compute the ideals and primary decomposition associated to Bayesian networks described in [1]. This library is implemented in a computer algebra system, Singular ([2]). This document describes how to use this library for the various computations. In Section 1, we show how to compute various ideals associated to Bayesian networks without hidden variables. I...

In this paper I propose a novel feature selection technique based on Bayesian networks. The main idea is to exploit the conditional independencies entailed by Bayesian networks in order to discard features that are not directly relevant for classification tasks. An algorithm for learning Bayesian networks and its use in feature selection are illustrated. The advantages of this algorithm with re...

One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use an...

We present techniques for importance sampling from distributions defined by Relational Bayesian Networks. The methods operate directly on the abstract representation language, and therefore can be applied in situations where sampling from a standard Bayesian Network representation is infeasible. We describe experimental results from using standard, adaptive and backward sampling strategies. Fur...

Dynamic Bayesian networks are a special type of Bayesian network that explicitly incorporate the dimension of time. They can be distinguished into repetitive and non-repetitive networks. Repetitiveness implies that the set of random variables of the network and their independence relations are the same at each time step. Due to their structural symmetry, repetitive networks are easier to use an...