Aggregative Efficiency of Bayesian Learning in Networks

An Introduction to Inference and Learning in Bayesian Networks

Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...

 Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization

A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this letter. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...

Learning efficiency of redundant neural networks in Bayesian estimation

This paper proves that the Bayesian stochastic complexity of a layered neural network is asymptotically smaller than that of a regular statistical model if it contains the true distribution. We consider a case when a three-layer perceptron with M input units, H hidden units and N output units is trained to estimate the true distribution represented by the model with H(0) hidden units and prove ...

 structure learning in bayesian networks using asexual reproduction optimization

a new structure learning approach for bayesian networks (bns) based on asexual reproduction optimization (aro) is proposed in this letter. aro can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. in aro, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...

Aspects of Efficiency Improvement in Bayesian Networks