# Bayesian Field Theory

Publication Year: 2003

Published by: The Johns Hopkins University Press

#### Cover

Download PDF (64.4 KB)

p. 1-1

#### Title Page, Copyright

Download PDF (200.4 KB)

pp. 2-5

#### Contents

Download PDF (278.6 KB)

pp. v-ix

#### Figures

Download PDF (212.5 KB)

pp. xi-xiv

#### Tables

Download PDF (189.2 KB)

pp. xv-17

#### Numerical Case Studies

Download PDF (138.9 KB)

pp. xvii-19

#### Acknowledgments and Abstract

Download PDF (208.1 KB)

pp. xix-xx

It is a great pleasure to thank Achim Weiguny for his support throughout this work, his careful reading of the manuscript, and him and Joerg Uhlig for the enjoyable collaboration. Essential parts of this work are based on the following articles of the author and these two collaborators...

#### Chapter 1. Introduction

Download PDF (312.9 KB)

pp. 3-8

Due to increasing computational resources, the last decade has seen a rapidly growing interest in applied empirical learning problems. They appear, for example, as density estimation, regression or classification problems and include, just to name a few, image reconstruction, speech recognition, time series prediction, object recognition...

#### Chapter 2. Bayesian framework

Download PDF (1.5 MB)

pp. 9-84

Looking for a scientific explanation of some phenomena means to search for a causal model which relates the relevant observations under study. To define a causal structure, observable (or visible) variables are separated into dependent variables ('measured effects', 'answers') and independent variables ('controlled causes', 'questions'). Dependent...

#### Chapter 3. Gaussian prior factors

Download PDF (1.5 MB)

pp. 85-165

In this chapter nonparametric density estimation problems will be studied working with Gaussian prior factors. The aim is to show that Gaussian prior factors are not only convenient from a technical point of view, but are also quite flexible and can be adapted in many ways to a specific learning task. Using this flexibility to implement...

#### Chapter 4. Parameterizing likelihoods: Variational methods

Download PDF (514.8 KB)

pp. 167-186

In this sense a MAP with a parametric model can be interpreted as a variational approach for a MAP for a nonparametric Bayesian problem. Clearly, minimal values obtained by minimization within a trial space can only be larger than or equal to the true minimal value, and from two variational approximations the one with smaller error is the...

#### Chapter 5. Parameterizing priors: Hyperparameters

Download PDF (960.9 KB)

pp. 187-227

The quality of nonparametric Bayesian approaches depends mainly on the
adequate implementation of problem specific *a priori* information. Especially
complex tasks with relatively few training data available, for example,
in speech recognition or image reconstruction, require task specific priors.
Choosing a simple Gaussian smoothness...

#### Chapter 6. Mixtures of Gaussian prior factors

Download PDF (800.2 KB)

pp. 229-256

Non-Gaussian prior factors which correspond to multimodal energy surfaces can be constructed or approximated by using mixtures of simpler prior components. In particular, it is convenient to use Gaussian densities as components or 'building blocks', since then many useful results obtained for Gaussian processes survive the generalization...

#### Chapter 7. Bayesian inverse quantum theory (BIQT)

Download PDF (1.2 MB)

pp. 257-308

The problem addressed in this chapter is the reconstruction of the Hamiltonians
or potentials of quantum systems from observational data. Finding
such 'causes' or 'laws' from a finite number of observations constitutes an
*inverse problem* and is typically *ill-posed* in the sense of Hadamard...

#### Chapter 8. Summary

Download PDF (217.4 KB)

pp. 309-312

In this book we wanted to develop a tool box for constructing prior models within a nonparametric Bayesian framework to empirical learning, and to exemplify its use for problems from different application areas. Nonparametric models, or field theories in the language of physics, allow typically a more explicit implementation of...

#### Appendix A: *A priori* information and *a posteriori* control

Download PDF (351.8 KB)

pp. 313-321

#### Appendix B: Probability, free energy, energy, information, entropy, and temperature

Download PDF (551.4 KB)

pp. 323-343

#### Appendix C: Iteration procedures: Learning

Download PDF (568.9 KB)

pp. 345-364

#### Bibliography

Download PDF (821.3 KB)

pp. 365-402

#### Index

Download PDF (709.7 KB)

pp. 403-411

E-ISBN-13: 9780801877971

E-ISBN-10: 0801877970

Print-ISBN-13: 9780801872204

Print-ISBN-10: 0801872200

Page Count: 432

Publication Year: 2003