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.

For undergradute social science majors. A textbook on the interpretation and use of research. Annotation copyright Book News, Inc. Portland, Or.

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

This handbook incorporates new developments in automation. It also presents a widespread and well-structured conglomeration of new emerging application areas, such as medical systems and health, transportation, security and maintenance, service, construction and retail as well as production or logistics. The handbook is not only an ideal resource for automation experts but also for people new to this expanding field.

Why the card catalog—a “paper machine” with rearrangeable elements—can be regarded as a precursor of the computer. Today on almost every desk in every office sits a computer. Eighty years ago, desktops were equipped with a nonelectronic data processing machine: a card file. In Paper Machines, Markus Krajewski traces the evolution of this proto-computer of rearrangeable parts (file cards) that became ubiquitous in offices between the world wars. The story begins with Konrad Gessner, a sixteenth-century Swiss polymath who described a new method of processing data: to cut up a sheet of handwritten notes into slips of paper, with one fact or topic per slip, and arrange as desired. In the late eighteenth century, the card catalog became the librarian's answer to the threat of information overload. Then, at the turn of the twentieth century, business adopted the technology of the card catalog as a bookkeeping tool. Krajewski explores this conceptual development and casts the card file as a “universal paper machine” that accomplishes the basic operations of Turing's universal discrete machine: storing, processing, and transferring data. In telling his story, Krajewski takes the reader on a number of illuminating detours, telling us, for example, that the card catalog and the numbered street address emerged at the same time in the same city (Vienna), and that Harvard University's home-grown cataloging system grew out of a librarian's laziness; and that Melvil Dewey (originator of the Dewey Decimal System) helped bring about the technology transfer of card files to business.

This book contains state-of-the-art research studies on the concepts, theory, processes, and real world applications of geographical information systems (GIS) in business. Its chapters are authored by many of the leading experts in applying GIS and geospatial science to business. The book utilizes a wide variety of approaches and methodologies including conceptual theory development, research frameworks, quantitative and qualitative methods, case studies, systems design, DSS theory, and geospatial analysis combined with point-of-sale. Since relatively little research has been published on GIS in business, this book is pioneering and should be the principal compendium of the latest research in this area. The book impacts not only the underlying definitions, concepts, and theories of GIS in business and industry, but its practice as well.