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