It is literature which draws on popular culture, and engages in speculation about science, history, and all varieties of social relations. This volume brings together essays by scholars and practitioners of science fiction, which look at the genre from different angles. It examines science fiction from Thomas More to the present day; and introduces important critical approaches (including Marxism, postmodernism, feminism and queer theory).

    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.

    The book concisely reviews key physiological principles and includes clinical correlations throughout to emphasize connections between basic physiology and clinical medicine. Numerous illustrations, tables, and flow charts help students visualize material quickly and aid in long-term retention. End-of-chapter USMLE-style questions and a comprehensive end-of-book exam test the student's problem-solving skills, and clearly explained answers guide the student through the correct steps in reasoning. This edition features increased coverage of pathophysiology, new questions, and a new two-color design and artwork.

    

    This book discuses various major types of financial institutions and financial instruments present along with how and why the system of money and capital markets is changing. It also provides a descriptive explanation of how interest rates and security values are determined.

    Using test-taking strategies meticulously developed by the author, the Review provides unique question dissection techniques, targeted key content review, 600 in-depth practice questions, and detailed, current exam information in a fast facts style. This second edition includes new chapters on pediatrics and adolescence and an extensive new section on geriatrics that encompasses body/metabolic changes, common disorders, and hospice/ethical considerations. The book also presents an expanded, intensive pharmacology review, 100 new exam questions, and is the only review to offer a new research chapter providing elements of research needed for E-B practice.The book reviews the complete lifespan from pediatrics to geriatrics and including pregnancy, and covers non-clinical content including ethics, medico-legal issues, advanced practice law, and reimbursement guidelines. The review of primary care disorders is organized by body system. The content is applicable for certification exams for both the ANCC and the AANP. This book is also a great companion to the FNP Certification Review app, coming soon for your iPhone or iPad!New to this edition: Reorganized for greater ease of useProvides extensive new section on geriatricsContains expanded coverage of pediatrics and adolescenceOffers expanded intensive-style pharmacology reviewPresents elements of research needed for E-B practiceIncludes 100 new questions for a title of 600 high-yield questionsWhy use this review?Delineates strategic question dissection techniques for study success developed by the author, a leading provider of exam preparation education for over 20 yearsProvides precisely targeted content reviewOffers 600 in-depth practice questions and detailed exam informationIncludes Exam Tips and Clinical Tips to help prioritize test content.It is the only book to present elements of research needed for E-B practiceOutlines normal findings and benign variants in physical assessment of each body systemIncludes succinct review of diseases commonly seen in primary careIt's a great companion to the forthcoming FNP Certification Review app!

    This unique framework encourages students to view the whole person as the sum of influences and effects of each of the domains of personality functioning. The second edition includes the latest research, as well as a restructuring of material, and continues to bring the subject to life by incorporating a vivid, four-color design.

    It includes extensive material on molecular biology, and presents biochemistry from a chemist's perspective.

    Each chapter of An Introduction to Brain and Behavior explores a specific question asked by working neuroscientists and students (i.e. "Why do we have a brain?"  "How is the nervous system organized?").  This approach brings coherence to a vast subject, and helps students understand what information is important as their study of brain and behavior progresses chapter to chapter.

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Do I have a strong enough case to go to trial? Will the Fed change interest rates? Can I trust this person? This book examines how people answer such questions. How do people cope with the complexities of the world economy, the uncertain behavior of friends and adversaries, or their own changing tastes and personalities? When are people's judgments prone to bias, and what is responsible for their biases? This book compiles psychologists' best attempts to answer these important questions.

    For courses in computer organization and architecture, this text provides a clear, comprehensive presentation of the organization and architecture of contemporary computers.