Doing Bayesian Data Analysis

Author: John Kruschke
Editor: Academic Press
ISBN: 0124059163
Size: 19,62 MB
Format: PDF, ePub, Docs
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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

Doing Bayesian Data Analysis

Author: John Kruschke
Editor: Academic Press
ISBN: 9780123814869
Size: 17,15 MB
Format: PDF, Kindle
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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

Doing Bayesian Data Analysis 2nd Edition

Author: John Kruschke
Editor:
ISBN:
Size: 13,78 MB
Format: PDF, ePub
Read: 336
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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.

Ict Innovations 2019 Big Data Processing And Mining

Author: Sonja Gievska
Editor: Springer Nature
ISBN: 3030331105
Size: 20,88 MB
Format: PDF, Kindle
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This book constitutes the refereed proceedings of the 11th International ICT Innovations Conference, ICT Innovations 2019, held in Ohrid, Macedonia, in October 2019. The 18 full papers presented were carefully reviewed and selected from 75 submissions. They cover the following topics: sensor applications and deployments, embedded and cyber-physical systems, robotics, network architectures, cloud computing, software infrastructure, software creation and management, models of computation, computational complexity and cryptography, design and analysis of algorithms, mathematical optimization, probability and statistics, data management systems, data mining, human computer interaction (HCI), artificial intelligence, machine learning, life and medical sciences, health care information systems, bioinformatics.

Advances In Parapsychological Research 9

Author: Stanley Krippner,
Editor: McFarland
ISBN: 0786471263
Size: 19,25 MB
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The essays in this volume 9 of a series inform readers of the present status of leading issues in parapsychology (or psi research). The authors are experts in their fields and have written in a reader-friendly way that captures the complexity and importance of their topics. Each essay comprehensively reviews a controversial topic from a critical stance, and updates its status based on the latest theoretical and empirical considerations. For readers who need to keep pace with the evolution of thinking in psi research, this volume is authoritative. The series as a whole has traced the development of psi research over decades, providing a longterm definitive resource.

R For Marketing Research And Analytics

Author: Chris Chapman
Editor: Springer
ISBN: 3319144367
Size: 16,84 MB
Format: PDF, ePub, Mobi
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This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.

Quantitative Genetics In The Wild

Author: Anne Charmantier
Editor: OUP Oxford
ISBN: 0191655961
Size: 19,90 MB
Format: PDF, Kindle
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Although the field of quantitative genetics - the study of the genetic basis of variation in quantitative characteristics such as body size, or reproductive success - is almost 100 years old, its application to the study of evolutionary processes in wild populations has expanded greatly over the last few decades. During this time, the use of 'wild quantitative genetics' has provided insights into a range of important questions in evolutionary ecology, ranging from studies conducting research in well-established fields such as life-history theory, behavioural ecology and sexual selection, to others addressing relatively new issues such as populations' responses to climate change or the process of senescence in natural environments. Across these fields, there is increasing appreciation of the need to quantify the genetic - rather than just the phenotypic - basis and diversity of key traits, the genetic basis of the associations between traits, and the interaction between these genetic effects and the environment. This research activity has been fuelled by methodological advances in both molecular genetics and statistics, as well as by exciting results emerging from laboratory studies of evolutionary quantitative genetics, and the increasing availability of suitable long-term datasets collected in natural populations, especially in animals. Quantitative Genetics in the Wild is the first book to synthesize the current level of knowledge in this exciting and rapidly-expanding area. This comprehensive volume also offers exciting perspectives for future studies in emerging areas, including the application of quantitative genetics to plants or arthropods, unraveling the molecular basis of variation in quantitative traits, or estimating non-additive genetic variance. Since this book deals with many fundamental questions in evolutionary ecology, it should be of interest to graduate, post-graduate students, and academics from a wide array of fields such as animal behaviour, ecology, evolution, and genetics.

Applied Logistic Regression

Author: David W. Hosmer, Jr.
Editor: John Wiley & Sons
ISBN: 1118548353
Size: 10,96 MB
Format: PDF, Mobi
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A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

Methods In Comparative Plant Population Ecology

Author: David Gibson
Editor: OUP Oxford
ISBN: 0191651230
Size: 17,88 MB
Format: PDF, ePub
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The field of plant population ecology has advanced considerably in the last decade since the first edition was published. In particular there have been substantial and ongoing advances in statistics and modelling applications in population ecology, as well as an explosion of new techniques reflecting the availability of new technologies (e.g. affordable and accurate Global Positioning Systems) and advances in molecular biology. This new edition has been updated and revised with more recent examples replacing older ones where appropriate. The book's trademark question-driven approach has been maintained and some important topics such as the metapopulation concept which are missing entirely from the current edition are now included throughout the text.