Probabilistic graphical models, Sucar, Luis Enrique
Автор: Thrun, Sebastian Название: Probabilistic robotics ISBN: 0262201623 ISBN-13(EAN): 9780262201629 Издательство: MIT Press Рейтинг: Цена: 95930.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
An introduction to the techniques and algorithms of the newest field in robotics.
Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. 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.
Автор: Liu Zhi-Qiang, Cai Jin-Hai, Buse Richard Название: Handwriting Recognition / Soft Computing and Probabilistic Approaches ISBN: 3540401776 ISBN-13(EAN): 9783540401773 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть Описание: This book takes a fresh look at the problem of unconstrained handwriting recognition and introduces the reader to new techniques for the recognition of written words and characters using statistical and soft computing approaches. The types of uncertainties and variations present in handwriting data are discussed in detail. The book presents several algorithms that use modified hidden Markov models and Markov random field models to simulate the handwriting data statistically and structurally in a single framework. The book explores methods that use fuzzy logic and fuzzy sets for handwriting recognition. The effectiveness of these techniques is demonstrated through extensive experimental results and real handwritten characters and words.
Автор: Enrique Castillo; Jose M. Gutierrez; Ali S. Hadi Название: Expert Systems and Probabilistic Network Models ISBN: 1461274818 ISBN-13(EAN): 9781461274810 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.
Автор: Nikolaos Limnios; Dumitru Cezar Ionescu Название: Statistical and Probabilistic Models in Reliability ISBN: 1461272807 ISBN-13(EAN): 9781461272809 Издательство: Springer Рейтинг: Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume consists of twenty-four papers selected by the editors from the sixty-one papers presented at the 1st International Conference on Mathemati cal Methods in Reliability held at the Politehnica University of Bucharest from 16 to 19 September 1997.
Автор: Guillermo L. G?mez M. Название: Dynamic Probabilistic Models and Social Structure ISBN: 9401051143 ISBN-13(EAN): 9789401051149 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In this way classical analytical mechanics was able to establish some general results, gaining insight through explicit solution of some simple cases and developing various methods of approximation for handling more complicated ones.
Автор: Neeraj Kumar, Arzoo Miglani Название: Probabilistic data structures for blockchain-based internet of things applications ISBN: 0367529904 ISBN-13(EAN): 9780367529901 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Невозможна поставка. Описание: This book covers theory and practical knowledge of probabilistic data structures (PDS) and blockchain (BC) concepts. It introduces the applicability of PDS in BC and each PDS has been explained through code snippets and illustrative examples. Further, it covers applications of PDS to BC along with implementation codes in Python.
Автор: Gilles Barthe, Joost-Pieter Katoen, Alexandra Silva Название: Foundations of probabilistic programming ISBN: 110848851X ISBN-13(EAN): 9781108488518 Издательство: Cambridge Academ Рейтинг: Цена: 61250.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.
Автор: Judea Pearl Название: Probabilistic Reasoning in Intelligent Systems, ISBN: 1558604790 ISBN-13(EAN): 9781558604797 Издательство: Elsevier Science Рейтинг: Цена: 63990.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.
The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
Автор: Whittaker, Joe Название: Graphical models in applied multivariate statistics ISBN: 0470743662 ISBN-13(EAN): 9780470743669 Издательство: Wiley Рейтинг: Цена: 70700.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: - It reveals the interrelationships between multiple variables and features of the underlying conditional independence. - It covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. - Many numerical examples and exercises with solutions are included.
Автор: Rina Dechter Название: Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms ISBN: 162705197X ISBN-13(EAN): 9781627051972 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 46200.00 T Наличие на складе: Невозможна поставка. Описание: Graphical models have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models.
Автор: Rina Dechter Название: Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms ISBN: 1681734907 ISBN-13(EAN): 9781681734903 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 72070.00 T Наличие на складе: Нет в наличии. Описание: Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
Автор: Rina Dechter Название: Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms ISBN: 1681734923 ISBN-13(EAN): 9781681734927 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 93330.00 T Наличие на складе: Нет в наличии. Описание: Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
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