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Machine Learning
book

Machine Learning

by Sergios Theodoridis
April 2015
Intermediate to advanced content levelIntermediate to advanced
1062 pages
40h 35m
English
Academic Press
Content preview from Machine Learning

12.9 Appendix to Chapter 12

In this appendix, a number of results concerning the Gaussian pdf are derived. The reader is advised to work on these derivations to get familiar with the tools that are heavily used in Bayesian inference. Because the derived formulas are applicable to different parts of the book and to different variables, we denote the involved random vectors as z and t and one can substitute notation accordingly, depending on the notational needs for each case.

12.9.1 PDFs with Exponent of Quadratic Form

Let

p(z)=expF(z),

si290_e  (12.110)

where

F(z)=12zTQz+zTp+C,

  (12.111)

where C is a constant and Q = QT and invertible. We rewrite ...

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Publisher Resources

ISBN: 9780128015223