Millions of users per day are affected by unsolicited email campaigns. During the last years several techniques to detect spam have been developed, achieving specially good results using machine learning algorithms. In this work we provide a baseline for a new spam filtering method. Carrying out this research we validate our hypothesis that personality recognition techniques can help in Bayesia...

In this paper, we describe results to model lateral and longitudinal control behavior of drivers with simple linear multiple regression models. This approach fits into the Bayesian Programming (BP) approach (Bessière, 2008) because the linear multiple regression model suggests an action selection strategy which is an alternative to the BP action selection strategies draw and best. Furthermore, ...

Traditional Compressive Sensing (CS) recovery techniques resorts a dictionary matrix to recover a signal. The success of recovery heavily relies on finding a dictionary matrix in which the signal representation is sparse. Achieving a sparse representation does not only depend on the dictionary matrix, but also depends on the data. It is a challenging issue to find an optimal dictionary to recov...

The Bayesian implementation of finite mixtures of distributions has been an area of considerable interest within the literature. Computational advances on approximation techniques such as Markov chain Monte Carlo (MCMC) methods have been a keystone to Bayesian analysis of mixture models. This paper deals with the Bayesian analysis of finite mixtures of two particular types of multidimensional d...

Legal reasoning can be approached from various perspectives, traditionally argumentation, probability and narrative. The communication between forensic experts and a judge or jury would benefit from an integration of these approaches. In previous papers we worked on the connection between the narrative and the probabilistic approach. We developed techniques for representing crime scenarios in a...

Numerous probability m o d e l s h a ve been suggested for information retrieval (IR) over the years. These models have been applied to try to manage the inherent uncertainty i n IR, for instance, document and query representation , relevance feedback, and evaluating the eeectiveness of IR system. On the other hand, Bayesian networks have become an established probabilistic framework for uncert...

Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be perform...

Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be given, in which case it is necessary to learn it if one wants to apply these techniques. We develop an algorithm for learning dynamic Bayesian network representations of Markov decision processes using data collected t...

Super-resolution of signals and images can improve the automatic detection and recognition of objects of interest. However, the uncertainty associated with this process is not often taken into consideration. This is important because the processing of noisy signals can result in spurious estimates of the scene content. This paper reviews a variety of super-resolution techniques and presents two...

The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. The reason is that simple Bayes is an extremely stable learning algorithm and most ensemble techniques such as bagging is mainly variance reduction techniques, thus not being able to benefit from its integration. However, simple Bayes can be effectively used in ensemble techniques, which perform also b...