universitetsområdet, Porsön
971 87 Luleå
Tel. 0920-49 18 78
Fax. 0920-49 21 91

senaste uppdatering är gjord: 2004-01-16


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Institutionen för systemteknik

Uppdatering av institutionens studiehandbok för 02/03

På den här sidan läggs fortlöpande in, de kompletteringar och ändringar som vi upptäckt behöver göras och även fel som någon annan upptäcker och meddelar till oss. 

2002-04-22 (sida 69)
Beskrivning av kursen SMD127  kom ej med i den tryckta versionen.


A Bayesian network is a graphical model that encodes 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 method in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the over fitting of data.
Over the last decade, the Bayesian network has become a popular representation for encoding uncertain expert knowledge in Expert Systems. More recently, researchers have developed methods for learning Bayesian networks from data. The techniques that have been developed are new and still evolving, but they have been shown to be remarkably effective for some data analysis problems.
In this course, we provide an introduction to Bayesian networks and associated Bayesian techniques for extracting and encoding knowledge from data. We discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve this models. With regards to latter task, we describe methods for learning both parameters and structure of Bayesian networks, 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.