Data analysis : a Bayesian tutorial ; [for scientists and engineers

Devinderjit Sivia, John Skilling

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مشخصات کتاب

نویسنده: Devinderjit Sivia, John Skilling
ناشر: Oxford University Press
سال انتشار: ۲۰۰۶
فرمت: PDF
زبان: انگلیسی
حجم فایل: ۶٫۱ مگابایت
شابک: 9780198568315، 9780198568322، 0198568312، 0198568320

دربارهٔ کتاب

This book is not really a tutorial for beginners as it goes directly into the subject. It is well written, rigorous, and not that expensive for people needing to learn the bayesian principles. For total beginners as I was, I would advise reading "Introduction to Bayesian Statistics" by Bolstad before this one. A good book on the topic, with good ideas and recent developments !
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'.

Focusing on Bayesian methods and maximum entropy, this book shows how a few fundamental rules can be used to tackle a variety of problems in data analysis. Topics covered include reliability analysis, multivariate optimisation, least-squares and maximum likelihood, and more

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