Expert Systems and Probabilistic Network Models, Enrique Castillo; Jose M. Gutierrez; Ali S. Hadi
Автор: Thrun, Sebastian Название: Probabilistic robotics ISBN: 0262201623 ISBN-13(EAN): 9780262201629 Издательство: MIT Press Рейтинг: Цена: 14390.00 р. 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.
Описание: This book proposes the formulation of an efficient methodology that estimates energy system uncertainty and predicts Remaining Useful Life (RUL) accurately with significantly reduced RUL prediction uncertainty.
Автор: Weiru Liu Название: Propositional, Probabilistic and Evidential Reasoning ISBN: 3790824933 ISBN-13(EAN): 9783790824933 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This facilitates purely symbolic reasoning using the possible worlds and numeric reasoning via the probabilities of those possible worlds. The consequence is a unified mechanism which includes both symbolic and numeric mechanisms as special cases.
Описание: On various examples ranging from geosciences to environmental sciences, thisbook explains how to generate an adequate description of uncertainty, how to justifysemiheuristic algorithms for processing uncertainty, and how to make these algorithmsmore computationally efficient.
Описание: This book presents novel learning techniques that enable mobile platforms with one or more robotic manipulators to autonomously adapt to new or changing situations.
Theory of conformal prediction.- Applications of conformal prediction.- Machine learning.
Автор: Hiromitsu Kumamoto Название: Satisfying Safety Goals by Probabilistic Risk Assessment ISBN: 1849966419 ISBN-13(EAN): 9781849966412 Издательство: Springer Рейтинг: Цена: 19589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a methodological approach to the goal-based safety design procedure that will soon be an international requirement. This is the first single volume book to describe how to satisfy safety goals by modern reliability engineering. Its focus is on the quantitative aspects of the international standards using a methodological approach.
Автор: Ben Goertzel; Matthew Ikl?; Izabela Freire Goertze Название: Probabilistic Logic Networks ISBN: 1441945784 ISBN-13(EAN): 9781441945785 Издательство: Springer Рейтинг: Цена: 21661.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. A broad scope of reasoning types are considered.
Автор: Milan Studeny Название: Probabilistic Conditional Independence Structures ISBN: 1849969485 ISBN-13(EAN): 9781849969482 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; The monograph presents the methods of structural imsets and supermodular functions, and deals with independence implication and equivalence of structural imsets.
The monographic volume addresses, in a systematic and comprehensive way, the state-of-the-art dependability (reliability, availability, risk and safety, security) of systems, using the Artificial Intelligence framework of Probabilistic Graphical Models (PGM). After a survey about the main concepts and methodologies adopted in dependability analysis, the book discusses the main features of PGM formalisms (like Bayesian and Decision Networks) and the advantages, both in terms of modeling and analysis, with respect to classical formalisms and model languages.
Methodologies for deriving PGMs from standard dependability formalisms will be introduced, by pointing out tools able to support such a process. Several case studies will be presented and analyzed to support the suitability of the use of PGMs in the study of dependable systems.
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