Incremental methods for Bayesian network learning
Josep Roure, Ramon Sangüesa I Sole · 1999
In this work we analyze the most relevant, in our opinion, algorithms for learning Bayesian Networks. We analyze methods that use goodness-of-fit tests between tentative networks and data. Within this sort of learning algorithms we distinguish batch and incremental methods. Finally, we propose a system, called BANDOLER, that incrementally learns Bayesian Networks from data and prior knowledge. The incremental fashion of the system allows to modify the learning strategy and to introduce new prior knowledge during the learning process in the light of the already learnt structure. 1 Introduction The aim of this work is twofold. On the one hand, we introduce the state of the art on learning Bayesian networks. It is intended to be a tutorial on the learning methods based on goodness-of-fit tests. We present the most significant, in our opinion, learning algorithms found in the literature, as well as the theory they are based on. On the other hand, we propose a research framework. The fiel...