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 ...
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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 ...
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Add this copy of Bayesian Logical Data Analysis for the Physical to cart. $35.00, very good condition, Sold by Twice Sold Tales rated 4.0 out of 5 stars, ships from Seattle, WA, UNITED STATES, published 2010 by Cambridge University Press.
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Near Fine. Octavo in wraps. Light edgewear; interior crisp and clean. **Due to size or weight of book additional charges for international and priority shipping may apply. **. 8vo-over 7¾-9¾" tall.
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