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Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica Support

Gregory, P. C.

Published by Cambridge University Press 2010-05, 2010

ISBN 10: 0521150124 ISBN 13: 9780521150125

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Bayesian Logical Data Analysis for the Physical Sciences : A Comparative Approach With Mathematica Support

Gregory, P. C.

Published by Cambridge University Press, 2010

ISBN 10: 0521150124 ISBN 13: 9780521150125

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Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica (R) Support

Phil Gregory

Published by Cambridge University Press, 2010

ISBN 10: 0521150124 ISBN 13: 9780521150125

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Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica� Support

Gregory, Phil

Published by Cambridge University Press, 2010

ISBN 10: 0521150124 ISBN 13: 9780521150125

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Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support

Phil Gregory

Published by Cambridge University Press, 2010

ISBN 10: 0521150124 ISBN 13: 9780521150125

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Bayesian Logical Data Analysis for the Physical Sciences (Paperback)

Phil Gregory

Published by Cambridge University Press, Cambridge, 2010

ISBN 10: 0521150124 ISBN 13: 9780521150125

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Paperback. Condition: new. Paperback. Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. Background material is provided in appendices and supporting Mathematica notebooks are available. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780521150125

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