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      Data Analysis: A Bayesian Tutorial

      2 in stock

      Firm sale: non returnable item
      SKU 9780198568322 Categories ,
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      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...

      £39.49

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      Description

      Product ID:9780198568322
      Product Form:Paperback / softback
      Country of Manufacture:GB
      Title:Data Analysis
      Subtitle:A Bayesian Tutorial
      Authors:Author: Devinderjit Sivia, John Skilling
      Page Count:264
      Subjects:Probability and statistics, Probability & statistics, Maths for scientists, Statistical physics, Maths for engineers, Maths for scientists, Statistical physics, Maths for engineers
      Description:Select Guide Rating
      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.
      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''.
      Imprint Name:Oxford University Press
      Publisher Name:Oxford University Press
      Country of Publication:GB
      Publishing Date:2006-06-01

      Additional information

      Weight420 g
      Dimensions232 × 157 × 16 mm