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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
Science undergraduates have come to accept the use of computers as commonplace. The daily use of portable sophisticated electronic calculators (some of them rivaling general-purpose minicomputers in their capa bi li ti es) has hastened this development. Over the past several years, computer assisted experimentation has assumed an important role in the experimental laboratory. Mini- and microcomputer systems have become an important part of the physical scientist's array of analytical instruments. Prompted by our beliefthat this was an inevitable development, we began several years aga to develop the curricular materials presented in this manual. At the outset, several objectives seemed important to uso First, insofar as possible, the experiments included should be thoroughly tested and error free. Second, they should be compatible with a variety of laboratory computer, data-acquisition, and control systems. Third, little or no previous background in either electronics or programming should be necessary. (Of course, such background would be advantageous. ) To satisfy these objectives, we decided to adopt a widespread high-level computer language, BASIC, suitably modified for the purpose. Furthermore, we have purposely avoided specifying any particular system or equipment. Rather, the functional characteristics of both hardware and software required are stipulated. The experiments have been developed using Varian 620 and Hewlett-Packard 2100 series computers, but we believe they are readily transferable to other commonly available computer systems with a minimum of difficulty.
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 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.
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
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.
Created to provide a safer and more cost effective lab environment, these innovative manuals introduce new methods to learning and understanding circuit analysis concepts by using Electronics Workbench to simulate actual lab experiments on the computer. Using the latest circuit simulation software, they allow for easy circuit modification, more extensive troubleshooting experiments, and more powerful computational tools. Readers work with circuits drawn on the computer screen and with simulated instruments that act like actual laboratory instruments. Circuits can be modified easily with on-screen editing, and analysis results provide fast, accurate feedback. The manuals provide extensive technical preparation for each interactive experiment, and a series of questions about the results of each experiment requires users to think about and to analyze the results of the experiments in more depth than is customary in other lab manuals. The manual examines diodes, bipolar transistors, field-effect transistors, operational amplifiers, amplifier frequency response, active filters, and oscillators. For individuals interested in fine tuning their knowledge of electronic devises using Electronics Workbench.
The accompanying CD-ROM includes all of the troubleshooting circuits and all of the circuits needed to perform the experiments.