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.

    Yet, while common, oak woodlands are anything but ordinary. In a book rich in illustration and suffused with wonder, author Kate Marianchild combines extensive research and years of personal experience to explore some of the marvelous plants and animals that the oak woodlands nurture. Acorn woodpeckers unite in marriages of up to ten mates and raise their young cooperatively. Ground squirrels roll in rattlesnake skins to hide their scent from hungry snakes. Manzanita's rust-colored, paper-thin bark peels away in time for the summer solstice, exposing sinuous contours that are cool to the touch even on the hottest day. Conveying up-to-the-minute scientific findings with a storyteller's skill, Marianchild introduces us to a host of remarkable creatures in a world close by, a world that rustles, hums, and sings with the sounds of wild things.

    Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. This book can be used for readers who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

    Belknap's Waterproof Grand Canyon River Guide (All New Color Edition)

    The Tiniest Mansion will teach you how to convert a small RV into a rolling palace with all the comforts of your home, plus the freedom to live anywhere you want without paying rent.The Tiniest Mansion covers everything from the essentials like choosing an RV, generating power, and dumping your tanks to more extravagant projects like installing marble floors and building an entertainment system.This book is a practical guide for anyone who is living in an RV or is considering it. Tynan, who has been living in an RV since 2006, shares all of his hard won secrets of RV living in this book.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    The gear you select and how you use it can mean big results—or bigger disappointment.FASTER takes a scientific look at triathlon to see what truly makes you faster—and busts the myths and doublespeak that waste your money and race times. In this fascinating exploration of the forces at play in the swim-bike-run sport, astronautical engineer and triathlete Jim Gourley shows where to find free speed, speed on a budget, and the gear upgrades that are worth it.FASTER offers specific, science-based guidance on the fastest techniques and the most effective gear, answering questions like: •    Which wetsuit is best for me? •    What’s the best way to draft a swimmer? •    Should I buy a lighter bike? •    Deep dish or disc wheels? •    Are lighter shoes faster? •    Who’s right about running technique? Gourley reviews published studies in peer-reviewed journals to show what scientists have learned about swim drafting, pacing the bike leg, race strategy for short and long-course racing, and the fastest ways to handle transitions.FASTER will change how you think about your body, your gear, and the world around you. With science on your side, you'll make the smart calls that will make you a better, faster triathlete.

    of Wisconsin-Madison) and Wichern (Texas A&M U.) present the newest edition of this college text on the statistical methods for describing and analyzing multivariate data, designed for students who have taken two or more statistics courses. The fifth edition includes the addition of seve

    However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gödel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.

    Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

    Indeed, such equations are crucial to mathematical physics. Although simplifications can be made that reduce these equations to ordinary differential equations, nevertheless the complete description of physical systems resides in the general area of partial differential equations.This highly useful text shows the reader how to formulate a partial differential equation from the physical problem (constructing the mathematical model) and how to solve the equation (along with initial and boundary conditions). Written for advanced undergraduate and graduate students, as well as professionals working in the applied sciences, this clearly written book offers realistic, practical coverage of diffusion-type problems, hyperbolic-type problems, elliptic-type problems, and numerical and approximate methods. Each chapter contains a selection of relevant problems (answers are provided) and suggestions for further reading.

    Louis are all intimately connected with the mysterious number e. In this informal and engaging history, Eli Maor portrays the curious characters and the elegant mathematics that lie behind the number. Designed for a reader with only a modest mathematical background, this biography brings out the central importance of e to mathematics and illuminates a golden era in the age of science.

    With an accessible and lively writing style, it introduces modern techniques to what can often be a formal and intimidating subject. Readers are led from the physics of flat spacetime (special relativity), through the intricacies of differential geometry and Einstein's equations, and on to exciting applications such as black holes, gravitational radiation, and cosmology.

    This recreational math book takes the reader on a fantastic voyage into the world of natural numbers. From the earliest discoveries of the ancient Greeks to various fundamental characteristics of the natural number sequence, Clawson explains fascinating mathematical mysteries in clear and easy prose. He delves into the heart of number theory to see and understand the exquisite relationships among natural numbers, and ends by exploring the ultimate mystery of mathematics: the Riemann hypothesis, which says that through a point in a plane, no line can be drawn parallel to a given line.While a professional mathematician's treatment of number theory involves the most sophisticated analytical tools, its basic ideas are surprisingly easy to comprehend. By concentrating on the meaning behind various equations and proofs and avoiding technical refinements, Mathematical Mysteries lets the common reader catch a glimpse of this wonderful and exotic world.

    From the Difference Engine to magic squares and from the Fibonacci rabbits to Fermat's Last Theorem, this book features the weird world of numbers, imaginary, real or infinite.