A tutorial on learning with Bayesian networks. (English) Zbl 0921.62029

Heckerman, David

Jordan, Michael I. (ed.), Learning in graphical models. Proceedings of the NATO ASI, Ettore Maiorana Centre, Erice, Italy, September 27 - October 7, 1996. Dordrecht: Kluwer Academic Publishers. NATO ASI Series. Series D. Behavioural and Social Sciences. 89, 301-354 (1998).

Summary: A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data.
We discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study.
For the entire collection see [Zbl 0889.00024].

MSC:

62F15	Bayesian inference
05C90	Applications of graph theory
62-09	Graphical methods in statistics (MSC2010)
68T05	Learning and adaptive systems in artificial intelligence

Keywords:

Gaussian approximation; EM-algorithm; model selection; marginal likelihood; search methods; Bayesian networks