Moving Beyond Non Informative Prior Distributions Achieving The Full Potential Of Bayesian Methods For Psychological Research
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Moving Beyond Non Informative Prior Distributions Achieving The Full Potential Of Bayesian Methods For Psychological Research
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Author : Christoph Koenig
language : en
Publisher: Frontiers Media SA
Release Date : 2022-02-01
Moving Beyond Non Informative Prior Distributions Achieving The Full Potential Of Bayesian Methods For Psychological Research written by Christoph Koenig and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-01 with Science categories.
Bayesian Structural Equation Modeling
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Author : Sarah Depaoli
language : en
Publisher: Guilford Publications
Release Date : 2021-08-16
Bayesian Structural Equation Modeling written by Sarah Depaoli and has been published by Guilford Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-16 with Social Science categories.
This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.
Novel Applications Of Bayesian And Other Models In Translational Neuroscience
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Author : Reza Rastmanesh
language : en
Publisher: Frontiers Media SA
Release Date : 2024-05-06
Novel Applications Of Bayesian And Other Models In Translational Neuroscience written by Reza Rastmanesh and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-06 with Science categories.
It has been proposed that the brain works in a Bayesian manner, and based on the free-energy principle, the brain's main function is to reduce environmental uncertainty; this is a proposed model as a universal principle governing adaptive brain function and structure. There are many pathophysiological, and clinical observations that can be easily explained by predictive Bayesian brain models. However, the novel applications of Bayesian models in translational neuroscience has been understudied and underreported. For example, variational Bayesian mixed-effects inference has been successfully tested for classification studies. A multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions has been recently publishe
Bayesian Data Analysis Third Edition
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Author : Andrew Gelman
language : en
Publisher: CRC Press
Release Date : 2013-11-01
Bayesian Data Analysis Third Edition written by Andrew Gelman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-01 with Mathematics categories.
Now 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 approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Informative Hypotheses
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Author : Herbert Hoijtink
language : en
Publisher: CRC Press
Release Date : 2011-10-26
Informative Hypotheses written by Herbert Hoijtink and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-26 with Mathematics categories.
When scientists formulate their theories, expectations, and hypotheses, they often use statements like: ``I expect mean A to be bigger than means B and C"; ``I expect that the relation between Y and both X1 and X2 is positive"; and ``I expect the relation between Y and X1 to be stronger than the relation between Y and X2". Stated otherwise, they formulate their expectations in terms of inequality constraints among the parameters in which they are interested, that is, they formulate Informative Hypotheses. There is currently a sound theoretical foundation for the evaluation of informative hypotheses using Bayes factors, p-values and the generalized order restricted information criterion. Furthermore, software that is often free is available to enable researchers to evaluate the informative hypotheses using their own data. The road is open to challenge the dominance of the null hypothesis for contemporary research in behavioral, social, and other sciences.
Proceedings Of The British Psychological Society
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Author : British Psychological Society
language : en
Publisher:
Release Date : 1993
Proceedings Of The British Psychological Society written by British Psychological Society and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Psychology categories.
Bayesian Analysis In Statistics And Econometrics
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Author : Donald A. Berry
language : en
Publisher: John Wiley & Sons
Release Date : 1996
Bayesian Analysis In Statistics And Econometrics written by Donald A. Berry and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Business & Economics categories.
This book is a definitive work that captures the current state of knowledge of Bayesian Analysis in Statistics and Econometrics and attempts to move it forward. It covers such topics as foundations, forecasting inferential matters, regression, computation and applications.
Doing Bayesian Data Analysis
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Author : John Kruschke
language : en
Publisher: Academic Press
Release Date : 2014-11-11
Doing Bayesian Data Analysis written by John Kruschke and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-11 with Mathematics categories.
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and JAGS 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 JAGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment - Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
Small Sample Size Solutions
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Author : Rens van de Schoot
language : en
Publisher: Routledge
Release Date : 2020-02-13
Small Sample Size Solutions written by Rens van de Schoot and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-13 with Psychology categories.
Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.
Bayesian Approaches To Clinical Trials And Health Care Evaluation
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Author : David J. Spiegelhalter
language : en
Publisher: John Wiley & Sons
Release Date : 2004-01-16
Bayesian Approaches To Clinical Trials And Health Care Evaluation written by David J. Spiegelhalter and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-16 with Mathematics categories.
READ ALL ABOUT IT! David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council’s biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries. Order a copy of this author’s comprehensive text TODAY! The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis. Covers a broad array of essential topics, building from the basics to more advanced techniques. Illustrated throughout by detailed case studies and worked examples Includes exercises in all chapters Accessible to anyone with a basic knowledge of statistics Authors are at the forefront of research into Bayesian methods in medical research Accompanied by a Web site featuring data sets and worked examples using Excel and WinBUGS - the most widely used Bayesian modelling package Bayesian Approaches to Clinical Trials and Health-Care Evaluation is suitable for students and researchers in medical statistics, statisticians in the pharmaceutical industry, and anyone involved in conducting clinical trials and assessment of health-care technology.