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'.
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Devinderjit Singh Sivia
Rutherford Appleton Laboratory
OX11 5DJ John Skilling
Maximum Entropy Data Consultants
42 Southgate Street
Bury St Edmonds
"Review from previous edition Providing a clear rationale for some of the most widely used procedures."--European Journal of Engineering Education
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Descripción OUP Oxford, 2016. Paperback. Estado de conservación: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Nº de ref. de la librería ria9780198568315_lsuk
Descripción Oxford University Press, 2006. Hardcover. Estado de conservación: New. Nº de ref. de la librería P110198568312