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210123 ||| eng |
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|a 9780123814869
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4 |
|a QA279.5
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| 100 |
1 |
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|a Kruschke, John
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| 245 |
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|a Doing Bayesian Data Analysis
|b a Tutorial Introduction with R and BUGS.
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| 260 |
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|a Burlington
|b Elsevier Science
|c 2010
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| 300 |
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|a 673 pages
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| 505 |
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|a Includes bibliographical references and index
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|a 7.7 ExercisesChapter 8. Inferring Two Binomial Proportions via Gibbs Sampling; 8.1 Prior, Likelihood, and Posterior for Two Proportions; 8.2 The Posterior via Exact Formal Analysis; 8.3 The Posterior via Grid Approximation; 8.4 The Posterior via Markov Chain Monte Carlo; 8.5 Doing It with BUGS; 8.6 How Different Are the Underlying Biases?; 8.7 Summary; 8.8 R Code; 8.9 Exercises; Chapter 9. Bernoulli Likelihood with Hierarchical Prior; 9.1 A Single Coin from a Single Mint; 9.2 Multiple Coins from a Single Mint; 9.3 Multiple Coins from Multiple Mints; 9.4 Summary; 9.5 R Code; 9.6 Exercises
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| 505 |
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|a 5.4 Summary: How to Do Bayesian Inference5.5 R Code; 5.6 Exercises; Chapter 6. Inferring a Binomial Proportion via Grid Approximation; 6.1 Bayes' Rule for Discrete Values of?; 6.2 Discretizing a Continuous Prior Density; 6.3 Estimation; 6.4 Prediction of Subsequent Data; 6.5 Model Comparison; 6.6 Summary; 6.7 R Code; 6.8 Exercises; Chapter 7. Inferring a Binomial Proportion via the Metropolis Algorithm; 7.1 A Simple Case of the Metropolis Algorithm; 7.2 The Metropolis Algorithm More Generally; 7.3 From the Sampled Posterior to the Three Goals; 7.4 MCMC in BUGS; 7.5 Conclusion; 7.6 R Code
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|a Chapter 10. Hierarchical Modeling and Model Comparison10.1 Model Comparison as Hierarchical Modeling; 10.2 Model Comparison in BUGS; 10.3 Model Comparison and Nested Models; 10.4 Review of Hierarchical Framework for Model Comparison; 10.5 Exercises; Chapter 11. Null Hypothesis Significance Testing; 11.1 NHST for the Bias of a Coin; 11.2 Prior Knowledge about the Coin; 11.3 Confidence Interval and Highest Density Interval; 11.4 Multiple Comparisons; 11.5 What a Sampling Distribution Is Good For; 11.6 Exercises; Chapter 12. Bayesian Approaches to Testing a Point ("Null") Hypothesis
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|a 3.1 The Set of All Possible Events3.2 Probability: Outside or Inside the Head; 3.3 Probability Distributions; 3.4 Two-Way Distributions; 3.5 R Code; 3.6 Exercises; Chapter 4. Bayes' Rule; 4.1 Bayes' Rule; 4.2 Applied to Models and Data; 4.3 The Three Goals of Inference; 4.4 R Code; 4.5 Exercises; Part 2: All the Fundamentals Applied to Inferring a Binomial Proportion; Chapter 5. Inferring a Binomial Proportion via Exact Mathematical Analysis; 5.1 The Likelihood Function: Bernoulli Distribution; 5.2 A Description of Beliefs: The Beta Distribution; 5.3 Three Inferential Goals
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|a Front Cover; Doing Bayesian Data Analysis; Copyright page; Dedication; Table of contents; Chapter 1. This Book's Organization: Read Me First!; 1.1 Real People Can Read This Book; 1.2 Prerequisites; 1.3 The Organization of This Book; 1.4 Gimme Feedback (Be Polite); 1.5 Acknowledgments; Part 1: The Basics: Parameters, Probability, Bayes' Rule, and R; Chapter 2. Introduction: Models We Believe In; 2.1 Models of Observations and Models of Beliefs; 2.2 Three Goals for Inference from Data; 2.3 The R Programming Language; 2.4 Exercises; Chapter 3. What Is This Stuff Called Probability?
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| 653 |
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|a R (Langage de programmation)
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| 653 |
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|a Bayesian statistical decision theory / http://id.loc.gov/authorities/subjects/sh85012506
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| 653 |
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|a R (Computer program language) / fast
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| 653 |
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|a Théorie de la décision bayésienne
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| 653 |
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|a Bayesian statistical decision theory / fast
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| 653 |
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|a R (Computer program language) / http://id.loc.gov/authorities/subjects/sh2002004407
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9780123814852
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| 776 |
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|z 0123814855
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9780123814852/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 519.5/42
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| 520 |
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|a There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all
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