statistical relational artificial intelligence (30 résultats)

hardcover. Etat : Good. Damage on front cover.

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Etat : New. In English.

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Etat : New. 1st edition NO-PA16APR2015-KAP.

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

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Paperback. Etat : New. Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the collaborative enhancement of understanding and applying theory and methods through the integration of models, cases, and code. The book employs StaRAI to address uncertainty analysis and modeling issues at different time scales in photovoltaic power generation, including photovoltaic power prediction, probabilistic power flow, stochastic planning, and more. Chapters cover uncertainty of PV power generation from short to long time scales, including day-ahead scheduling (24 hours in advance), intraday scheduling (minute to hour rolling), and grid planning (15 years).Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, RandD, and designers.

Etat : New. Explores how Statistical Relational Artificial Intelligence (StaRAI) can be applied to photovoltaic power prediction, maintenance, and planningProvides a theoretical framework supported by schematic diagrams, real examples, and code.

Paperback. Etat : New. Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the collaborative enhancement of understanding and applying theory and methods through the integration of models, cases, and code. The book employs StaRAI to address uncertainty analysis and modeling issues at different time scales in photovoltaic power generation, including photovoltaic power prediction, probabilistic power flow, stochastic planning, and more. Chapters cover uncertainty of PV power generation from short to long time scales, including day-ahead scheduling (24 hours in advance), intraday scheduling (minute to hour rolling), and grid planning (15 years).Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, RandD, and designers.

Taschenbuch. Etat : Neu. Neuware - Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the collaborative enhancement of understanding and applying theory and methods through the integration of models, cases, and code. The book employs StaRAI to address uncertainty analysis and modeling issues at different time scales in photovoltaic power generation, including photovoltaic power prediction, probabilistic power flow, stochastic planning, and more. Chapters cover uncertainty of PV power generation from short to long time scales, including day-ahead scheduling (24 hours in advance), intraday scheduling (minute to hour rolling), and grid planning (15 years).Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, R&D, and designers.

Etat : new. Questo è un articolo print on demand.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks. 192 pp. Englisch.

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with u.

Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 192 pp. Englisch.

Etat : new. Questo è un articolo print on demand.

Paperback. Etat : new. Paperback. Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the collaborative enhancement of understanding and applying theory and methods through the integration of models, cases, and code. The book employs StaRAI to address uncertainty analysis and modeling issues at different time scales in photovoltaic power generation, including photovoltaic power prediction, probabilistic power flow, stochastic planning, and more. Chapters cover uncertainty of PV power generation from short to long time scales, including day-ahead scheduling (24 hours in advance), intraday scheduling (minute to hour rolling), and grid planning (15 years).Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, R&D, and designers. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Etat : Brand New. 350 pages. 9.00x6.00x9.03 inches. In Stock. This item is printed on demand.

Paperback. Etat : new. Paperback. Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis addresses uncertainty issues in photovoltaic power generation while also supporting the collaborative enhancement of understanding and applying theory and methods through the integration of models, cases, and code. The book employs StaRAI to address uncertainty analysis and modeling issues at different time scales in photovoltaic power generation, including photovoltaic power prediction, probabilistic power flow, stochastic planning, and more. Chapters cover uncertainty of PV power generation from short to long time scales, including day-ahead scheduling (24 hours in advance), intraday scheduling (minute to hour rolling), and grid planning (15 years).Other sections study the impact of photovoltaic uncertainty on the power grid, offering the most classic cases of probabilistic load flow and PV stochastic planning.The theoretical content of this book is not only systematic but supplemented with concrete examples and MATLAB/Python codes. Its contents will be of interest to all those working on photovoltaic planning, power generation, power plants, and applications of AI, including researchers, advanced students, faculty engineers, R&D, and designers. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Etat : new. Paperback. Statistical Relational Artificial Intelligence in Photovoltaic Power Uncertainty Analysis This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.