Software prediction unveils itself as a difficult but important task which can aid the manager on decision making, possibly allowing for time and resources sparing. One of the approaches set forth to perform this task has been the application of machine learning techniques. Bayesian networks are perhaps one of the most promising of these. In this context, we present an assessment of automatic B...

Knowledge-driven and data-driven methods are the two representative categories of intelligent technologies used in fault diagnosis nuclear power plants. have advantages interpretability robustness, while better performance ease modeling inference efficiency. Given complementarity methods, a combination them is worthwhile investigation. In this work, we introduce new techniques based on Bayesian...

This thesis is about how Bayesian methods can be applied to explicitly model and efficiently reason about uncertainty to make optimal recommendations. We are interested in three dimensions of recommender systems: (1) preference elicitation, (2) set-based recommendations, and (3) matchmaking. The first dimension concerns how one can minimize the elicitation efforts in learning a user’s utility f...

Variable selection is a topic of great importance in high-dimensional statistical modeling and has a wide range of real-world applications. Many variable selection techniques have been proposed in the context of linear regression, and the Lasso model is probably one of the most popular penalized regression techniques. In this paper, we propose a new, fully hierarchical, Bayesian version of the ...

50 AI MAGAZINE u n d e r s t a n d i n g (Charniak and Goldman 1989a, 1989b; Goldman 1990), vision (Levitt, Mullin, and Binford 1989), heuristic search (Hansson and Mayer 1989), and so on. It is probably fair to say that Bayesian networks are to a large segment of the AI-uncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be the...

The biggest challenge for agents’ collaboration in Decision Support Systems is resolving possible conflicts of knowledge. When coordinating activities, either in a cooperative or a competitive environment, conflicts may arise and three basic strategies to solve these conflicts are by means of negotiation, mediation and arbitration. Following these strategies; different intelligent techniques de...

The purpose of this paper is threefold. First, it briefly introduces basic Bayesian techniques with emphasis on present applications in sensor networks. Second, it reviews modern Bayesian simulation methods, thereby providing an introduction to the main building blocks of the advanced Markov chain Monte Carlo and Sequential Monte Carlo methods. Lastly, it discusses new interesting research hori...

We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task data using Bayesian techniques. We describe an implementation of this framework which uses variational Bayesian mixtures of factor analyzers in order to attack classification problems in high-dimensional spaces where lab...

We are interested in Bayesian nonparametric Hidden Markov Models. More precisely, we are going to prove the consistency of these models under appropriate conditions on the prior distribution and when the number of states of the Markov Chain is finite and known. Our approach is based on exponential forgetting and usual Bayesian consistency techniques.