Books like Bayesian data analysis by Andrew Gelman

The Elements of Statistical Learning explains key statistical methods that underlie modern machine learning, data mining, and bioinformatics, covering supervised and unsupervised learning techniques such as classification trees, neural networks, and support vector machines.

This book presents the role of likelihood in a whole range of statistical problems, from a simple comparison of two accident rates to complex studies requiring generalized linear or semiparametric modeling. The book emphasizes that the likelihood is not simply a device to produce an estimate, but more importantly it is a tool for modeling. The book generally takes an informal approach, where most important results are established using heuristic arguments and motivated with realistic examples. With currently available computing power, examples are not contrived to allow a closed analytical solution, and the book concentrates on the statistical aspects of the data modelling. In addition to classical likelihood theory, the book covers many modern topics such as generalized linear models, generalized linear mixed models, nonparametric smoothing, robustness, EM algorithm and empirical likelihood. --back cover

A concise textbook that blends the fundamentals of Shannon‐based information theory with practical applications in data compression, error‑correction, and machine learning, featuring tools such as message‑passing, Monte Carlo, and variational methods.

A concise reference of statistical tables that accompany each entry with a pointer to the relevant section of F. James Rohlf’s textbook *Biometry*, offering explanations and practical applications for biometric data analysis.

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

This book presents a new approach to the conceptual basis of all visual art derived from his work, and while it is about making movies—the catch-all for video, film, computer graphics and anything else that may appear to move—the thrust of this book is a radical redefinition of all visual media, including traditional standards like painting. The framework these notes propose is a way of thinking about visual art that eliminates all former media in favor of a division based on our ability to see movement or change in a work of art. While most movies change and move rapidly, this understanding is equally concerned with the very slow, or apparently immobile. Produced as a result of his own experimental movies, Structuring Time draws on more than a decade’s worth of direct, practical applications of these ideas. The way Betancourt presents his understanding of media makes this book a useful guide for the beginner, and a fascinating approach for the established artist. The series of topics proceed from the very simple, almost obvious aspect of movies, and gradually elaborate a logical framework able to include everything from Hollywood and commercial TV to Bill Viola's slow-motion installations, and even LED signs. This conception of media art also presents possibilities for new developments that exceed anything that exists now.

A comprehensive reference dictionary covering terminology, concepts, and methods in statistics, used by scholars in the social sciences.

An introductory textbook that explores the intersection of artificial intelligence and statistical methods, offering mathematical foundations, expert system concepts, and case studies for students and practitioners.

A concise, mathematically rigorous guide to statistical inference covering point estimation, hypothesis testing, interval estimation, Bayesian methods, decision theory, non-parametrics, robust techniques, generalized linear models, and computationally intensive approaches for advanced undergraduates.

A comprehensive guide to Bayesian estimation techniques in mathematical statistics, with applications in various distributions and regression analysis.