Data Analysis

Clicks: 2
ID: 302180
2006
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Abstract
Abstract 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'.
Reference Key
openalex_W4388247078 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors D.S. Sivia
Journal Oxford University Press eBooks
Year 2006
DOI
10.1093/oso/9780198568315.001.0001
URL
https://doi.org/10.1093/oso/9780198568315.001.0001
Keywords Keywords not found

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