Active Learning of Causal Bayes Net Structure

Mind Change Optimal Learning of Bayes Net Structure

This paper analyzes the problem of learning the structure of a Bayes net (BN) in the theoretical framework of Gold’s learning paradigm. Bayes nets are one of the most prominent formalisms for knowledge representation and probabilistic and causal reasoning. We follow constraint-based approaches to learning Bayes net structure, where learning is based on observed conditional dependencies between ...

A theory of causal learning in children: causal maps and Bayes nets.

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed gra...

Mechanisms of theory formation in young children.

Research suggests that by the age of five, children have extensive causal knowledge, in the form of intuitive theories. The crucial question for developmental cognitive science is how young children are able to learn causal structure from evidence. Recently, researchers in computer science and statistics have developed representations (causal Bayes nets) and learning algorithms to infer causal ...

Mind change optimal learning of Bayes net structure from dependency and independency data

Article history: Received 8 July 2008 Revised 12 March 2009 Available online 3 May 2009 This paper analyzes the problem of learning the structure of a Bayes net in the theoretical framework of Gold’s learning paradigm. Bayes nets are one of the most prominent formalisms for knowledge representation and probabilistic and causal reasoning. We follow constraint-based approaches to learning Bayes n...

A tive Learning of Causal Bayes Net Stru ture Kevin