Karl-Rudolf Koch Introduction to Bayesian Statistics Second Edition

Transcription

1 Karl-Rudolf Koch Introduction to Bayesian Statistics Second Edition

2 Karl-Rudolf Koch Introduction to Bayesian Statistics Second, updated and enlarged Edition With 17 Figures

3 Professor Dr.-Ing., Dr.-Ing. E.h. mult. Karl-Rudolf Koch (em.) University of Bonn Institute of Theoretical Geodesy Nussallee Bonn Library of Congress Control Number: ISBN Springer Berlin Heidelberg New York ISBN (1. Aufl) Einführung in Bayes-Statistik This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable to prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com Springer-Verlag Berlin Heidelberg 2007 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: deblik, Berlin Production: Almas Schimmel Typesetting: Camera-ready by Author Printed on acid-free paper 30/3180/as

4 Preface to the Second Edition This is the second and translated edition of the German book Einführung in die Bayes-Statistik, Springer-Verlag, Berlin Heidelberg New York, It has been completely revised and numerous new developments are pointed out together with the relevant literature. The Chapter is extended by the stochastic trace estimation for variance components. The new Chapter presents the estimation of the regularization parameter of type Tykhonov regularization for inverse problems as the ratio of two variance components. The reconstruction and the smoothing of digital three-dimensional images is demonstrated in the new Chapter 5.3. The Chapter on importance sampling for the Monte Carlo integration is rewritten to solve a more general integral. This chapter contains also the derivation of the SIR (samplingimportance-resampling) algorithm as an alternative to the rejection method for generating random samples. Markov Chain Monte Carlo methods are now frequently applied in Bayesian statistics. The first of these methods, the Metropolis algorithm, is therefore presented in the new Chapter The kernel method is introduced in Chapter 6.3.3, to estimate density functions for unknown parameters, and used for the example of Chapter As a special application of the Gibbs sampler, finally, the computation and propagation of large covariance matrices is derived in the new Chapter I want to express my gratitude to Mrs. Brigitte Gundlich, Dr.-Ing., and to Mr. Boris Kargoll, Dipl.-Ing., for their suggestions to improve the book. I also would like to mention the good cooperation with Dr. Chris Bendall of Springer-Verlag. Bonn, March 2007 Karl-Rudolf Koch

5 Preface to the First German Edition This book is intended to serve as an introduction to Bayesian statistics which is founded on Bayes theorem. By means of this theorem it is possible to estimate unknown parameters, to establish confidence regions for the unknown parameters and to test hypotheses for the parameters. This simple approach cannot be taken by traditional statistics, since it does not start from Bayes theorem. In this respect Bayesian statistics has an essential advantage over traditional statistics. The book addresses readers who face the task of statistical inference on unknown parameters of complex systems, i.e. who have to estimate unknown parameters, to establish confidence regions and to test hypotheses for these parameters. An effective use of the book merely requires a basic background in analysis and linear algebra. However, a short introduction to onedimensional random variables with their probability distributions is followed by introducing multidimensional random variables so that the knowledge of one-dimensional statistics will be helpful. It also will be of an advantage for the reader to be familiar with the issues of estimating parameters, although the methods here are illustrated with many examples. Bayesian statistics extends the notion of probability by defining the probability for statements or propositions, whereas traditional statistics generally restricts itself to the probability of random events resulting from random experiments. By logical and consistent reasoning three laws can be derived for the probability of statements from which all further laws of probability may be deduced. This will be explained in Chapter 2. This chapter also contains the derivation of Bayes theorem and of the probability distributions for random variables. Thereafter, the univariate and multivariate distributions required further along in the book are collected though without derivation. Prior density functions for Bayes theorem are discussed at the end of the chapter. Chapter 3 shows how Bayes theorem can lead to estimating unknown parameters, to establishing confidence regions and to testing hypotheses for the parameters. These methods are then applied in the linear model covered in Chapter 4. Cases are considered where the variance factor contained in the covariance matrix of the observations is either known or unknown, where informative or noninformative priors are available and where the linear model is of full rank or not of full rank. Estimation of parameters robust with respect to outliers and the Kalman filter are also derived. Special models and methods are given in Chapter 5, including the model of prediction and filtering, the linear model with unknown variance and covariance components, the problem of pattern recognition and the segmentation of

6 VIII Preface digital images. In addition, Bayesian networks are developed for decisions in systems with uncertainties. They are, for instance, applied for the automatic interpretation of digital images. If it is not possible to analytically solve the integrals for estimating parameters, for establishing confidence regions and for testing hypotheses, then numerical techniques have to be used. The two most important ones are the Monte Carlo integration and the Markoff Chain Monte Carlo methods. They are presented in Chapter 6. Illustrative examples have been variously added. The end of each is indicated by the symbol, and the examples are numbered within a chapter if necessary. For estimating parameters in linear models traditional statistics can rely on methods, which are simpler than the ones of Bayesian statistics. They are used here to derive necessary results. Thus, the techniques of traditional statistics and of Bayesian statistics are not treated separately, as is often the case such as in two of the author s books Parameter Estimation and Hypothesis Testing in Linear Models, 2nd Ed., Springer-Verlag, Berlin Heidelberg New York, 1999 and Bayesian Inference with Geodetic Applications, Springer-Verlag, Berlin Heidelberg New York, By applying Bayesian statistics with additions from traditional statistics it is tried here to derive as simply and as clearly as possible methods for the statistical inference on parameters. Discussions with colleagues provided valuable suggestions that I am grateful for. My appreciation is also forwarded to those students of our university who contributed ideas for improving this book. Equally, I would like to express my gratitude to my colleagues and staff of the Institute of Theoretical Geodesy who assisted in preparing it. My special thanks go to Mrs. Brigitte Gundlich, Dipl.-Ing., for various suggestions concerning this book and to Mrs. Ingrid Wahl for typesetting and formatting the text. Finally, I would like to thank the publisher for valuable input. Bonn, August 1999 Karl-Rudolf Koch

7 Contents 1 Introduction 1 2 Probability Rules of Probability Deductive and Plausible Reasoning Statement Calculus Conditional Probability Product Rule and Sum Rule of Probability Generalized Sum Rule Axioms of Probability Chain Rule and Independence Bayes Theorem Recursive Application of Bayes Theorem Distributions Discrete Distribution Continuous Distribution Binomial Distribution Multidimensional Discrete and Continuous Distributions Marginal Distribution Conditional Distribution Independent Random Variables and Chain Rule Generalized Bayes Theorem Expected Value, Variance and Covariance Expected Value Variance and Covariance Expected Value of a Quadratic Form Univariate Distributions Normal Distribution Gamma Distribution Inverted Gamma Distribution Beta Distribution χ 2 -Distribution F -Distribution t-distribution Exponential Distribution Cauchy Distribution Multivariate Distributions Multivariate Normal Distribution Multivariate t-distribution

8 X Contents Normal-Gamma Distribution Prior Density Functions Noninformative Priors Maximum Entropy Priors Conjugate Priors Parameter Estimation, Confidence Regions and Hypothesis Testing Bayes Rule Point Estimation Quadratic Loss Function Loss Function of the Absolute Errors Zero-One Loss Estimation of Confidence Regions Confidence Regions Boundary of a Confidence Region Hypothesis Testing Different Hypotheses Test of Hypotheses Special Priors for Hypotheses Test of the Point Null Hypothesis by Confidence Regions 82 4 Linear Model Definition and Likelihood Function Linear Model with Known Variance Factor Noninformative Priors Method of Least Squares Estimation of the Variance Factor in Traditional Statistics Linear Model with Constraints in Traditional Statistics Robust Parameter Estimation Informative Priors Kalman Filter Linear Model with Unknown Variance Factor Noninformative Priors Informative Priors Linear Model not of Full Rank Noninformative Priors Informative Priors Special Models and Applications Prediction and Filtering Model of Prediction and Filtering as Special Linear Model

9 Contents XI Special Model of Prediction and Filtering Variance and Covariance Components Model and Likelihood Function Noninformative Priors Informative Priors Variance Components Distributions for Variance Components Regularization Reconstructing and Smoothing of Three-dimensional Images Positron Emission Tomography Image Reconstruction Iterated Conditional Modes Algorithm Pattern Recognition Classification by Bayes Rule Normal Distribution with Known and Unknown Parameters Parameters for Texture Bayesian Networks Systems with Uncertainties Setup of a Bayesian Network Computation of Probabilities Bayesian Network in Form of a Chain Bayesian Network in Form of a Tree Bayesian Network in Form of a Polytreee Numerical Methods Generating Random Values Generating Random Numbers Inversion Method Rejection Method Generating Values for Normally Distributed Random Variables Monte Carlo Integration Importance Sampling and SIR Algorithm Crude Monte Carlo Integration Computation of Estimates, Confidence Regions and Probabilities for Hypotheses Computation of Marginal Distributions Confidence Region for Robust Estimation of Parameters as Example Markov Chain Monte Carlo Methods Metropolis Algorithm Gibbs Sampler Computation of Estimates, Confidence Regions and Probabilities for Hypotheses

10 XII Contents Computation of Marginal Distributions Gibbs Sampler for Computing and Propagating Large Covariance Matrices Continuation of the Example: Confidence Region for Robust Estimation of Parameters References 235 Index 245