Abstract
Assume that you know the structure of a Bayesian network model over the variables \( \mathcal{U} \), but you do not have any estimates for the conditional probabilities. On the other hand, you have access to a database of cases, i.e., a set of simultaneous values for some of the variables in \( \mathcal{U} \). You can now use these cases to estimate the parameters of the model, namely the conditional probabilities. In this chapter we consider two approaches for handling this problem: First we show how a database of cases can be used to estimate the parameters once and for all (so-called batch learning). After that we shall investigate the situation where the cases are accumulated sequentially, i.e., we would like to adapt the model as each new case arrives. The reader is expected to be familiar with Section 1.5.
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© 2007 Springer Science +Business Media, LLC
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(2007). Parameter estimation. In: Bayesian Networks and Decision Graphs. Information Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68282-2_6
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DOI: https://doi.org/10.1007/978-0-387-68282-2_6
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-68281-5
Online ISBN: 978-0-387-68282-2
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