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ISBN 10: 0521150124 ISBN 13: 9780521150125
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Lingua: Inglese
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ISBN 10: 0521150124 ISBN 13: 9780521150125
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Aggiungi al carrelloCondizione: New. A clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005. Num Pages: 488 pages, 132 b/w illus. 74 exercises. BIC Classification: PBT; PGC. Category: (P) Professional & Vocational. Dimension: 245 x 175 x 25. Weight in Grams: 834. . 2010. Illustrated. paperback. . . . .
Lingua: Inglese
Editore: Cambridge University Press, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
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Aggiungi al carrelloCondizione: New. A clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005. Num Pages: 488 pages, 132 b/w illus. 74 exercises. BIC Classification: PBT; PGC. Category: (P) Professional & Vocational. Dimension: 245 x 175 x 25. Weight in Grams: 834. . 2010. Illustrated. paperback. . . . . Books ship from the US and Ireland.
Lingua: Inglese
Editore: Cambridge University Press, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
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Lingua: Inglese
Editore: Cambridge University Press, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
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Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
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Paperback. Condizione: 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Editore: Cambridge University Press, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
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Editore: Cambridge University Press, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
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Lingua: Inglese
Editore: Cambridge University Press, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 488 132 Illus.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
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Aggiungi al carrelloPaperback. Condizione: 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Lingua: Inglese
Editore: Cambridge University Press, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 126,66
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Lingua: Inglese
Editore: Cambridge University Press, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
Da: moluna, Greven, Germania
EUR 93,90
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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. B.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2010
ISBN 10: 0521150124 ISBN 13: 9780521150125
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 136,26
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Aggiungi al carrelloPaperback. Condizione: 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.