Abstract—A criterion based on mutual information among variables is proposed for building a bayesian tree from a finite number of examples. The factor graph, in Forney-style form, can be used as an associative memory that performs probabilistic inference in data fusion applications. The procedure is explained with the aid of a fully-described example
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data s...
This paper presents a Bayesian method for constructing probabilistic networks from databases. In par...
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data s...
Abstract—A criterion based on mutual information among variables is proposed for building a bayesian...
The paper shows how to build an associative memory from a finite list of ex-amples. By means of a ful...
The general problem of computing posterior probabilities in Bayesian networds is NP-hard (Cooper 199...
The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increa...
AbstractThe present paper introduces a new kind of representation for the potentials in a Bayesian n...
Factor graphs provide a convenient framework for automatically generating (approximate) Bayesian inf...
Bayesian networks can be seen as a factorisation of a joint probability distribution over a set of v...
We relax parametric inference to a nonparametric representation towards more general solutions on fa...
The main goal of a relatively new scientific discipline, known as Knowledge Discovery in Databases o...
Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a co...
This copy of the thesis has been supplied on condition that anyone who consults it is understood to ...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data s...
This paper presents a Bayesian method for constructing probabilistic networks from databases. In par...
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data s...
Abstract—A criterion based on mutual information among variables is proposed for building a bayesian...
The paper shows how to build an associative memory from a finite list of ex-amples. By means of a ful...
The general problem of computing posterior probabilities in Bayesian networds is NP-hard (Cooper 199...
The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increa...
AbstractThe present paper introduces a new kind of representation for the potentials in a Bayesian n...
Factor graphs provide a convenient framework for automatically generating (approximate) Bayesian inf...
Bayesian networks can be seen as a factorisation of a joint probability distribution over a set of v...
We relax parametric inference to a nonparametric representation towards more general solutions on fa...
The main goal of a relatively new scientific discipline, known as Knowledge Discovery in Databases o...
Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a co...
This copy of the thesis has been supplied on condition that anyone who consults it is understood to ...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data s...
This paper presents a Bayesian method for constructing probabilistic networks from databases. In par...
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data s...