Bayesian Statistics For Experimental Scientists
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Bayesian Statistics for Experimental Scientists
Author | : Richard A. Chechile |
Publsiher | : MIT Press |
Total Pages | : 473 |
Release | : 2020-09-08 |
Genre | : Mathematics |
ISBN | : 9780262360708 |
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An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis. This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only well-developed methods for doing Bayesian statistics but also novel tools that enable Bayesian statistical analyses for cases that previously did not have a full Bayesian solution. The book's premise is that there are fundamental problems with orthodox frequentist statistical analyses that distort the scientific process. Side-by-side comparisons of Bayesian and frequentist methods illustrate the mismatch between the needs of experimental scientists in making inferences from data and the properties of the standard tools of classical statistics.
The Subjectivity of Scientists and the Bayesian Approach
Author | : S. James Press,Judith M. Tanur |
Publsiher | : Courier Dover Publications |
Total Pages | : 292 |
Release | : 2016-03-16 |
Genre | : Mathematics |
ISBN | : 9780486802848 |
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Originally published: New York: John Wiley & Sons, Inc., 2001.
Bayesian Data Analysis
Author | : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin |
Publsiher | : CRC Press |
Total Pages | : 663 |
Release | : 2013-11-27 |
Genre | : Mathematics |
ISBN | : 9781439898208 |
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Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied
Bayesian Statistics for the Social Sciences
Author | : David Kaplan |
Publsiher | : Guilford Publications |
Total Pages | : 275 |
Release | : 2023-10-02 |
Genre | : Social Science |
ISBN | : 9781462553556 |
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The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package. The text covers Hamiltonian Monte Carlo, Bayesian linear regression and generalized linear models, model evaluation and comparison, multilevel modeling, models for continuous and categorical latent variables, missing data, and more. Concepts are fully illustrated with worked-through examples from large-scale educational and social science databases, such as the Program for International Student Assessment and the Early Childhood Longitudinal Study. Annotated RStan code appears in screened boxes; the companion website (www.guilford.com/kaplan-materials) provides data sets and code for the book's examples. New to This Edition *Utilizes the R interface to Stan--faster and more stable than previously available Bayesian software--for most of the applications discussed. *Coverage of Hamiltonian MC; Cromwell’s rule; Jeffreys' prior; the LKJ prior for correlation matrices; model evaluation and model comparison, with a critique of the Bayesian information criterion; variational Bayes as an alternative to Markov chain Monte Carlo (MCMC) sampling; and other new topics. *Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling.
The Nature of Scientific Evidence
Author | : Mark L. Taper,Subhash R. Lele |
Publsiher | : University of Chicago Press |
Total Pages | : 586 |
Release | : 2010-12-15 |
Genre | : Science |
ISBN | : 9780226789583 |
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An exploration of the statistical foundations of scientific inference, The Nature of Scientific Evidence asks what constitutes scientific evidence and whether scientific evidence can be quantified statistically. Mark Taper, Subhash Lele, and an esteemed group of contributors explore the relationships among hypotheses, models, data, and inference on which scientific progress rests in an attempt to develop a new quantitative framework for evidence. Informed by interdisciplinary discussions among scientists, philosophers, and statisticians, they propose a new "evidential" approach, which may be more in keeping with the scientific method. The Nature of Scientific Evidence persuasively argues that all scientists should care more about the fine points of statistical philosophy because therein lies the connection between theory and data. Though the book uses ecology as an exemplary science, the interdisciplinary evaluation of the use of statistics in empirical research will be of interest to any reader engaged in the quantification and evaluation of data.
Doing Bayesian Data Analysis
Author | : John Kruschke |
Publsiher | : Academic Press |
Total Pages | : 673 |
Release | : 2010-11-25 |
Genre | : Mathematics |
ISBN | : 9780123814869 |
Download Doing Bayesian Data Analysis Book in PDF, Epub and Kindle
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and BUGS software Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). Coverage of experiment planning R and BUGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment
Bayesian Data Analysis for the Behavioral and Neural Sciences
Author | : Todd E. Hudson |
Publsiher | : Cambridge University Press |
Total Pages | : 500 |
Release | : 2021-06-30 |
Genre | : Psychology |
ISBN | : 1108812902 |
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This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond "frequentist" concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called "hypothesis testing") problems most frequently encountered in real-world applications.
Bayesian Statistics for Social Scientists
Author | : Lawrence D. Phillips |
Publsiher | : Unknown |
Total Pages | : 472 |
Release | : 1974 |
Genre | : Bayesian statistical decision theory |
ISBN | : UCAL:B5000270 |
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