Codes, Systems, and Graphical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Codes, Systems, and Graphical Models write by Brian Marcus. This book was released on 2012-12-06. Codes, Systems, and Graphical Models available in PDF, EPUB and Kindle. Coding theory, system theory, and symbolic dynamics have much in common. A major new theme in this area of research is that of codes and systems based on graphical models. This volume contains survey and research articles from leading researchers at the interface of these subjects.
Probabilistic Graphical Models
Probabilistic Graphical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Probabilistic Graphical Models write by Daphne Koller. This book was released on 2009-07-31. Probabilistic Graphical Models available in PDF, EPUB and Kindle. A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. 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.
Graphical Models for Machine Learning and Digital Communication
Graphical Models for Machine Learning and Digital Communication - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Graphical Models for Machine Learning and Digital Communication write by Brendan J. Frey. This book was released on 1998. Graphical Models for Machine Learning and Digital Communication available in PDF, EPUB and Kindle. Content Description. #Includes bibliographical references and index.
Learning in Graphical Models
Learning in Graphical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learning in Graphical Models write by M.I. Jordan. This book was released on 2012-12-06. Learning in Graphical Models available in PDF, EPUB and Kindle. In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.
Graphical Models
Graphical Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Graphical Models write by Steffen L. Lauritzen. This book was released on 1996-05-02. Graphical Models available in PDF, EPUB and Kindle. The idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables. The use of graphical models in statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides the first comprehensive and authoritative account of the theory of graphical models and is written by a leading expert in the field. It contains the fundamental graph theory required and a thorough study of Markov properties associated with various type of graphs. The statistical theory of log-linear and graphical models for contingency tables, covariance selection models, and graphical models with mixed discrete-continous variables in developed detail. Special topics, such as the application of graphical models to probabilistic expert systems, are described briefly, and appendices give details of the multivarate normal distribution and of the theory of regular exponential families. The author has recently been awarded the RSS Guy Medal in Silver 1996 for his innovative contributions to statistical theory and practice, and especially for his work on graphical models.
