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Computer vision:
models, learning and inference
Simon J.D. Prince
July 7, 2012
Copyright
c
2011, 2012 by Simon Prince; to be published by Cambridge University
Press 2012. For personal use only, not for distribution.
The most recent version of this book can be downloaded from
http://www.computervisionmodels.com.
Please mail errata to s.prince@cs.ucl.ac.uk.
2
Copyright
c
2011,2012 by Simon Prince; published by Cambridge University Press 2012.
For personal use only, not for distribution.
This book is dedicated to Richard Eagle, without whom it would never have been
started, and to Lynfa Stroud, without whom it would never have been finished.
2
Copyright
c
2011,2012 by Simon Prince; published by Cambridge University Press 2012.
For personal use only, not for distribution.
Contents
1 Introduction 15
I Probability 21
2 Introduction to probability 25
2.1 Random variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2 Joint probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3 Marginalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4 Conditional probability . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.5 Bayes’ rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.6 Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.7 Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3 Common probability distributions 35
3.1 Bernoulli distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Beta distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 Categorical distribution . . . . . . . . . . . . . . . . . . . . . . . . 38
3.4 Dirichlet distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5 Univariate normal distribution . . . . . . . . . . . . . . . . . . . . 40
3.6 Normal-scaled inverse gamma distribution . . . . . . . . . . . . . . 40
3.7 Multivariate normal distribution . . . . . . . . . . . . . . . . . . . 41
3.8 Normal inverse Wishart distribution . . . . . . . . . . . . . . . . . 42
3.9 Conjugacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4 Fitting probability models 49
4.1 Maximum likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 Maximum a posteriori . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3 The Bayesian approach . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4 Worked example 1: univariate normal . . . . . . . . . . . . . . . . 51
4.5 Worked example 2: categorical distribution . . . . . . . . . . . . . 60
5 The normal distribution 69
5.1 Types of covariance matrix . . . . . . . . . . . . . . . . . . . . . . 69
5.2 Decomposition of covariance . . . . . . . . . . . . . . . . . . . . . . 71
5.3 Linear transformations of variables . . . . . . . . . . . . . . . . . . 72
Copyright
c
2011,2012 by Simon Prince; published by Cambridge University Press 2012.
For personal use only, not for distribution.
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