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Modeling and Inverse Problems in the Presence of Uncertainty
book

Modeling and Inverse Problems in the Presence of Uncertainty

by H. T. Banks, Shuhua Hu, W. Clayton Thompson
April 2014
Intermediate to advanced content levelIntermediate to advanced
405 pages
13h
English
Chapman and Hall/CRC
Content preview from Modeling and Inverse Problems in the Presence of Uncertainty
122 Modeling and Inverse Problems in the Presence of Uncertainty
FIGURE 4.3: Akaike informa tion criterio n: r elationship between
Kullback–Leibler information and the maximum value of the log-likelihood
function of a given approximating model.
4.2.1 Kullback–Leibler Information
Kullback–Leibler (K–L) information [29] is a well-known measure of “dis -
tance” betwe en two probability distributio n models. Hence, it can be used
to measure information lost when an approximating probability distribution
model is used to approximate the true probability distribution model. L et
p
0
denote the probability distribution model that actually generates the data
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Publisher Resources

ISBN: 9781482206432