Data Analysis A Bayesian Tutorial Oxford Science Publications BOOK

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Book Title
Data Analysis: A Bayesian Tutorial (Oxford Science Publications)
ISBN
9780198518891
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
Oxford University Press, Incorporated
ISBN-10
0198518897
ISBN-13
9780198518891
eBay Product ID (ePID)
586818

Product Key Features

Number of Pages
200 Pages
Language
English
Publication Name
Data Analysis : a Bayesian Tutorial
Publication Year
1996
Subject
Mechanical, Probability & Statistics / Bayesian Analysis
Type
Textbook
Subject Area
Mathematics, Technology & Engineering
Author
D. S. Sivia
Format
Uk-Trade Paper

Dimensions

Item Height
0.3 in
Item Weight
11.3 Oz
Item Length
9.2 in
Item Width
6.1 in

Additional Product Features

Intended Audience
College Audience
LCCN
96-005426
Dewey Edition
20
Reviews
"This book is designed to be a guide to the Bayesian approach. It is certainly not an all-encompassing textbook on the subject but rather describes for the reader how one can use the Bayesian approach for standard data analyses. . . .Well written and at a modest technical level (senior undergraduate)." --Technometrics"Sivia's tutorial explains the Bayesian approach for analyzing experimental data. In particular, stress is placed on modern developments such as maximum entropy."--Choice, "This book is designed to be a guide to the Bayesian approach. It is certainly not an all-encompassing textbook on the subject but rather describes for the reader how one can use the Bayesian approach for standard data analyses. . . .Well written and at a modest technical level (senior undergraduate)." -- Technometrics "Sivia's tutorial explains the Bayesian approach for analyzing experimental data. In particular, stress is placed on modern developments such as maximum entropy."-- Choice, '...covers a broad range of applications of Bayesian methods at a fairly high mathematical level.'Robert Matthews, New Scientist, September 1996, "This book is designed to be a guide to the Bayesian approach. It is certainly not an all-encompassing textbook on the subject but rather describes for the reader how one can use the Bayesian approach for standard data analyses. . . .Well written and at a modest technical level (senior undergraduate)." --Technometrics "Sivia's tutorial explains the Bayesian approach for analyzing experimental data. In particular, stress is placed on modern developments such as maximum entropy."--Choice, ... this book takes the mystery out of statistics by providing a clear rationale for some of the most widely-used procedures.
Illustrated
Yes
Dewey Decimal
519.5
Table Of Content
1. The basics2. Parameter Estimation I3. Parameter Estimation II4. Model Selection5. Assigning Probabilities6. Non-parametric Estimation7. Experimental DesignAppendix
Synopsis
Statistics lectures have often been viewed with trepidation by engineering and science students taking an ancillary course in this subject. Whereas there are many texts showing "how" statistical methods are applied, few provide a clear explanation for non-statisticians of how the principles of data analysis can be based on probability theory. Data Analysis: A Bayesian Tutorial provides such a text, putting emphasis as much on understanding "why" and "when" certain statistical procedures should be used as "how". This difference in approach makes the text ideal 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. With its central emphasis on a few fundamental rules, this book takes the mystery out of statistics by providing a clear rationale for some of the most widely-used procedures., This is the first book on the maximum entropy and Bayesian methods 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 wide variety of problems in data analysis. 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. As a logical and unified approach to the subject of data analysis, with a self-contained tutorial approach, this work will be valued by instructors and students alike.
LC Classification Number
QA279.5.S55 1996

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