Data Analysis

Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. <p/>This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis multivariate optimization least-squares and maximum likelihood error-propagation hypothesis testing maximum entropy and experimental design. <p/>The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure allowing for the straightforward handling of outliers and unknown correlated noise and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.