Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address question...
In this paper, we propose a post randomization technique to learn a Bayesian network (BN) from distributed heterogeneous data, in a privacy sensitive fashion. In this case, two or ...
This paper treats automated detection of road and lane boundaries by fusing information from forwardlooking optical and active W-band radar imaging sensors mounted on a motor vehi...
ct. A Bayesian framework for genetic programming GP is presented. This is motivated by the observation that genetic programming iteratively searches populations of fitter programs ...
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analys...