Articoli correlati a Data Analysis: A Bayesian Tutorial [Lingua inglese]
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Sinossi
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'.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Recensione
One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. (Katie St. Clair MAA Reviews)
L'autore
Devinderjit Singh Sivia Rutherford Appleton Laboratory Chilton Oxon OX11 5DJ John Skilling Maximum Entropy Data Consultants 42 Southgate Street Bury St Edmonds Suffolk IP33 2AZ
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreOxford University Press
- Data di pubblicazione2006
- ISBN 10 0198568320
- ISBN 13 9780198568322
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero edizione2
- Numero di pagine260
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Data Analysis: A Bayesian Tutorial
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Paperback. Condizione: new. Paperback. 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 illustratedwith a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximumlikelihood, 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'. This is the second edition of the first tutorial book on Bayesian methods and maximum entropy aimed at senior undergraduates in science and engineering. It takes the mystery out of statistics by showing how a few fundamental rules can be used to tackle a variety of problems in data analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780198568322
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