Priors on network structures : biasing the search for Bayesian networks
In this paper we show how a user can influence recovery of Bayesian Networks from a database by specifying prior knowledge. The main novelty of our approach is that the user only has to provide partial prior knowledge, which is then completed to a full prior over all possible network structures. This partial prior knowledge is expressed among variables in an intuitive pairwise way, which embodies the uncertainty of the user about his/her own prior knowledge. Thus, the uncertainty of the model is updated in the normal Bayesian way.
|Information Search and Retrieval (acm H.3.3), Problem Solving, Control Methods, and Search (acm I.2.8), Model Development (acm I.6.5), PROBABILITY AND STATISTICS (acm G.3)|
|Foundations and philosophical topics (msc 62A01), Applications in probability theory and statistics (msc 46N30), Applications in probability theory and statistics (msc 47N30), Probability theory on algebraic and topological structures (msc 60Bxx), Search theory (msc 90B40), Mathematical modeling (models of systems, model-matching, etc.) (msc 93A30), Data analysis (msc 62-07)|
|Information (theme 2)|
|Information Systems [INS]|
Castelo, J.R, & Siebes, A.P.J.M. (1998). Priors on network structures : biasing the search for Bayesian networks. Information Systems [INS]. CWI.