Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    Real-life cases and situations demonstrate the significance of ethics in the world of criminal justice today for those studying or contemplating a career in criminal justice.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    QRI-5

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

    Covering all of the fundamentals in a straightforward, student-friendly manner, it is ideal for undergraduate- and graduate-level courses across the social sciences and also serves as an indispensable guide for researchers. Striking a balance between specific techniques and the underlying logic of scientific inquiry, this book provides a lucid treatment of the four major approaches to research: experimentation, survey research, field research, and the use of available data. Richly developed examples of empirical research and an emphasis on the research process enable students to better understand the real-world application of research methods. The authors also offer a unique chapter (13) advocating a multiple-methods strategy.

    This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.

    It is the ideal solution for the academic setting, multiple-day training, or individuals in need of more extensive food safety training. Food safety has never been more important to the restaurant industry and its customers. Based on the "2013""FDA Food Code, " the "ServSafe Coursebook" focuses on the preventative measures to keep food safe. The content in the "ServSafe Coursebook" goes beyond the principles found in the "ServSafe""Manager Book" and adds greater depth and breadth of food safety practices by featuring expanded sections on food defense, high-risk populations, active managerial control, and crisis management. Food safety topics are presented in a user-friendly, practical way with more real-world case studies and stories to help readers understand the day-to-day importance of food safety. Developed by the industry, for the industry, ServSafe(R) is a proven way to minimize risk and maximize protection for foodservice owners, employees, and customers. Recognized as the industry standard, ServSafe offers a complete suite of printed and online products and is the most important ingredient to food safety training and certification success. The "ServSafe Coursebook" is available packaged with MyServSafeLab(TM). MyServSafeLab is an online homework, tutorial, and assessment program that truly engages students in learning. It helps students better prepare for class, quizzes, and exams-resulting in better performance in the course-and provides educators a dynamic set of tools for gauging individual and class progress. The "ServSafe Coursebook" 6th Edition is available packaged in a number of ways to suit your specific needs. ISBN: 0133883507 is a package containing the book and the answer sheet for the pencil/paper version of the ServSafe Food Protection Manager exam" ServSafe Coursebook with Answer Sheet 6th Edition " Also available: Stand-alone book: "ServSafe Coursebook "6th Edition ISBN: 0133883604A package containing the book, the answer sheet for the pencil/paper version of the ServSafe Food Protection Manager exam, AND MyServSafeLab with Pearson eText Access Card: " ServSafe Coursebook with Answer Sheet, Revised Plus NEW MyServSafeLab with Pearson eText -- Access Card Package, "6/e ISBN: 0133962733A package containing the book and online exam voucher: "ServSafe Coursebook with Online Exam Voucher 6th Edition "ISBN: 0133883515A package containing the book, online exam voucher, AND MyServSafeLab with Pearson eText Access Card: "ServSafe Coursebook with Online Exam Voucher, Revised Plus NEW MyServSafeLab with Pearson eText -- Access Card Package, 6/e "ISBN: 0133962725Students, if interested in purchasing this title with MyServSafeLab, ask your instructor for the correct package ISBN. MyServSafeLab is not a self-paced technology and should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

    This highly visual, non- technical survey emphasizes broad, up-to-date coverage of basic topics and principles in geology, oceanography, meteorology, and astronomy. The text's flexible design lends itself to the diversity of Earth science courses in both content and approach. As in previous editions, the main focus is to foster student understanding of basic earth science principles.

    This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

    Anyone involved in the work or study of today's tourism industry must consider all these factors together and their effect on it.This updated and revised Eleventh Edition of Tourism presents a comprehensive introduction to travel and tourism, while continuing the tested approach of successful previous editions. New and revised coverage, integrating the latest developments in the tourism industry, includes:Profiles of such industry leaders as J. W. Marriott, Jr, of Marriott International, Inc., James Rasulo of Walt Disney Parks and Resorts, and Francesco Frangialli, Secretary-General of the United Nations World Tourism Organization Lively and fun "Global Insights" look at emerging areas of importance in tourism Demographic trends, such as how the many new travelers from BRIC nations (Brazil, Russia, India, and China) recreate and seek culture New information on transportation options, including high-speed rail and river cruises New consideration of the use of the Internet, particularly Web 2.0 (podcasting, social networks, and blogs), in tourism research, marketing, and promotion Updates on passport, visa, and governmental policies Expanded treatment of crisis management Moving easily between theory and practice, Tourism, Eleventh Edition offers an unparalleled discussion of recreational travel today, and is suitable both as a lively learning tool for students and a reliable go-to reference for tourism professionals.

    The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

    This user-friendly training guide includes groups of instructional exercises and practice tests on various aspects and features of the sixth edition of the Publication Manual, including electronic references and citations, grammar, headings, seriation, statistical and mathematical copy, italics, capitalization, numbers style, and table formatting.