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.

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 analyses go beyond frequentist techniques of p-values and null hypothesis tests, providing a modern understanding of data analysis.

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.

Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

Do you find statistics overwhelming and confusing? Have you ever wished for someone to explain the basics in a clear and easy-to-follow style? This accessible textbook gives a step-by-step introduction to all the topics covered in introductory statistics courses for the behavioural sciences, with plenty of examples discussed in depth, based on real psychology experiments utilising the statistical techniques described. Advanced sections are also provided, for those who want to learn a particular topic in more depth. Statistics for the Behavioural Sciences: An Introduction begins with an introduction to the basic concepts, before providing a detailed explanation of basic statistical tests and concepts such as descriptive statistics, probability, the binomial distribution, continuous random variables, the normal distribution, the Chi-Square distribution, the analysis of categorical data, t-tests, correlation and regression. This timely and highly readable text will be invaluable to undergraduate students of psychology, and students of research methods courses in related disciplines, as well as anyone with an interest in the basic concepts and tests associated with statistics in the behavioural sciences.

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

Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.

This book brings together perspectives on predictive processing and expected experience. It features contributions from an interdisciplinary group of authors specializing in philosophy, psychology, cognitive science, and neuroscience. Predictive processing, or predictive coding, is the theory that the brain constantly minimizes the error of its predictions based on the sensory input it receives from the world. This process of prediction error minimization has numerous implications for different forms of conscious and perceptual experience. The chapters in this volume explore these implications and various phenomena related to them. The contributors tackle issues related to precision estimation, sensory prediction, probabilistic perception, and attention, as well as the role predictive processing plays in emotion, action, psychotic experience, anosognosia, and gut complex. Expected Experiences will be of interest to scholars and advanced students in philosophy, psychology, and cognitive science working on issues related to predictive processing and coding.