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This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.
Recent developments in computer technology are providing historians with new ways to see—and seek to hear, touch, or smell—traces of the past. Place-based augmented reality applications are an increasingly common feature at heritage sites and museums, allowing historians to create immersive, multifaceted learning experiences. Now that computer vision can be directed at the past, research involving thousands of images can recreate lost or destroyed objects or environments, and discern patterns in vast datasets that could not be perceived by the naked eye. Seeing the Past with Computers is a collection of twelve thought-pieces on the current and potential uses of augmented reality and computer vision in historical research, teaching, and presentation. The experts gathered here reflect upon their experiences working with new technologies, share their ideas for best practices, and assess the implications of—and imagine future possibilities for—new methods of historical study. Among the experimental topics they explore are the use of augmented reality that empowers students to challenge the presentation of historical material in their textbooks; the application of seeing computers to unlock unusual cultural knowledge, such as the secrets of vaudevillian stage magic; hacking facial recognition technology to reveal victims of racism in a century-old Australian archive; and rebuilding the soundscape of an Iron Age village with aural augmented reality. This volume is a valuable resource for scholars and students of history and the digital humanities more broadly. It will inspire them to apply innovative methods to open new paths for conducting and sharing their own research.
This book describes methods for designing and analyzing experiments that are conducted using a computer code, a computer experiment, and, when possible, a physical experiment. Computer experiments continue to increase in popularity as surrogates for and adjuncts to physical experiments. Since the publication of the first edition, there have been many methodological advances and software developments to implement these new methodologies. The computer experiments literature has emphasized the construction of algorithms for various data analysis tasks (design construction, prediction, sensitivity analysis, calibration among others), and the development of web-based repositories of designs for immediate application. While it is written at a level that is accessible to readers with Masters-level training in Statistics, the book is written in sufficient detail to be useful for practitioners and researchers. New to this revised and expanded edition: • An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples • A new comparison of plug-in prediction methodologies for real-valued simulator output • An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions • A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization • A new chapter describing graphical and numerical sensitivity analysis tools • Substantial new material on calibration-based prediction and inference for calibration parameters • Lists of software that can be used to fit models discussed in the book to aid practitioners
Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice.
Computer simulations based on mathematical models have become ubiquitous across the engineering disciplines and throughout the physical sciences. Successful use of a simulation model, however, requires careful interrogation of the model through systematic computer experiments. While specific theoretical/mathematical examinations of computer experim
This book consists of four parts with 32 chapters adapted for four short courses, from the basic to the advanced levels of medical statistics (biostatistics), ideal for biomedical students. Part 1 is a compulsory course of Basic Statistics with descriptive statistics, parameter estimation and hypothesis test, simple correlation and regression. Part 2 is a selective course on Study Design and Implementation with sampling survey, interventional study, observational study, diagnosis study, data sorting and article writing. Part 3 is a specially curated course of Multivariate Analyses with complex analyses of variance, variety of regressions and classical multivariate analyses. Part 4 is a seminar course on Introduction to Advanced Statistical Methods with meta-analysis, time series, item response theory, structure equation model, multi-level model, bio-informatics, genetic statistics and data mining.The main body of each chapter is followed by five practical sections: Report Writing, Case Discrimination, Computer Experiments, Frequently Asked Questions and Summary, and Practice & Think. Moreover, there are 2 attached Appendices, Appendix A includes Introductions to SPSS, Excel and R respectively, and Appendix B includes all the programs, data and printouts for Computer Experiments in addition to the Tests for Review and the reference answers for Case Discrimination as well as Practice & Think..This book can be used as a textbook for biomedical students at both under- and postgraduate levels. It can also serve as an important guide for researchers, professionals and officers in the biomedical field.
Together with the internet site, this book is ideally suited for independent and remote study Web site is kept to date and guest educational institutions are invited to join in creating their own lab modules on different device aspects First such program Reputation of the authors who are leaders in the field of semiconductor electronics
Neuroinformatics presents cutting-edge techniques for the synergistic study of neuroinformatics. The book facilitates the efforts of discovering neuroscience through the sharing of data and the use of computational models. It demonstrates the use of neuroinformatic components as a mechanism for understanding complex disorders. It contains detailed explanations, advantages, and disadvantages of traditional and non-invasive imaging methods.
Electronic and Experimental Music: Technology, Music, and Culture provides a comprehensive history of electronic music, covering key composers, genres, and techniques used in analog and digital synthesis. This textbook has been extensively revised with the needs of students and instructors in mind. The reader-friendly style, logical organization, and pedagogical features of the fifth edition allow easy access to key ideas, milestones, and concepts. New to this edition: • A companion website, featuring key examples of electronic music, both historical and contemporary. • Listening Guides providing a moment-by-moment annotated exploration of key works of electronic music. • A new chapter—Contemporary Practices in Composing Electronic Music. • Updated presentation of classic electronic music in the United Kingdom, Italy, Latin America, and Asia, covering the history of electronic music globally. • An expanded discussion of early experiments with jazz and electronic music, and the roots of electronic rock. • Additional accounts of the vastly under-reported contributions of women composers in the field. • More photos, scores, and illustrations throughout. The companion website features a number of student and instructor resources, such as additional Listening Guides, links to streaming audio examples and online video resources, PowerPoint slides, and interactive quizzes.