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.
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.
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'.