This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.

    Each sketch contains Questions and Projects to help you learn more about its topic and to see how its main ideas fit into the bigger picture of history. The 25 short stories are preceded by a 56-page bird's-eye overview of the entire panorama of mathematical history, a whirlwind tour of the most important people, events, and trends that shaped the mathematics we know today. Reading suggestions after each sketch provide starting points for readers who want to pursue a topic further."

    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.

    Provides a review of introductory noncalculus-based physics for those who do not have a strong background in mathematics.

    Grounded in the latest knowledge and filled with illustrative examples from diverse institutions, as well as helpful reflection questions, the book's guidance can be put to immediate use. In addition, the authors suggest ways of avoiding common pitfalls and address what to do when faced with budget fluctuations and changing fiscal environments."This book is vitally important for understanding the complex financial underpinnings of higher education. Could there be a more critical time for administrators to add to their knowledge in this area? I don't think so." --EUGENE S. SUNSHINE, senior vice president for business and finance, Northwestern University"The authors have produced an easily readable and valuable resource for board members, administrators, students, faculty, or anyone interested in knowing about budgeting and the budgeting process. Their treatment of the subject is thorough and complete." --LARRY H. DIETZ, vice chancellor for student affairs, Southern Illinois University, Carbondale"This is the best 'nitty-gritty-how-to' book on university budgeting that I have found. My graduate students at both the master's and doctoral levels have found it to be a comprehensive, insightful, and useful tool in their graduate studies." --LINDA KUK, program chair, Higher Education Graduate Programs, and associate professor of education, Colorado State University