Uncertainty constraints decision making (52 Ergebnisse)

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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 316 | Sprache: Englisch | Produktart: Bücher | This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil engineering, education, psychology and decision making, and religion¿and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas.Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.…

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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 480 | Sprache: Englisch | Produktart: Bücher | In the first approximation, decision making is nothing else but an optimization problem: We want to select the best alternative. This description, however, is not fully accurate: it implicitly assumes that we know the exact consequences of each decision, and that, once we have selected a decision, no constraints prevent us from implementing it. In reality, we usually know the consequences with some uncertainty, and there are also numerous constraints that needs to be taken into account. The presence of uncertainty and constraints makes decision making challenging. To resolve these challenges, we need to go beyond simple optimization, we also need to get a good understanding of how the corresponding systems and objects operate, a good understanding of why we observe what we observe ¿ this will help us better predict what will be the consequences of different decisions. All these problems ¿ in relation to different application areas ¿ are the main focus of this book.…

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil engineering, education, psychology and decision making, and religion-and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas.Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil engineering, education, psychology and decision making, and religion-and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas.Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.…

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the first approximation, decision making is nothing else but an optimization problem:We want to select the best alternative. This description, however, is not fullyaccurate: it implicitly assumes that we know the exact consequences of each decision,and that, once we have selected a decision, no constraints prevent us fromimplementing it. In reality, we usually know the consequences with some uncertainty,and there are also numerous constraints that needs to be taken into account.The presence of uncertainty and constraints makes decision making challenging.To resolve these challenges, we need to go beyond simple optimization, we alsoneed to get a good understanding of how the corresponding systems and objectsoperate, a good understanding of why we observe what we observe - this will helpus better predict what will be the consequences of different decisions. All theseproblems - in relation to different application areas - are the main focus of thisbook.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the first approximation, decision making is nothing else but an optimization problem:We want to select the best alternative. This description, however, is not fullyaccurate: it implicitly assumes that we know the exact consequences of each decision,and that, once we have selected a decision, no constraints prevent us fromimplementing it. In reality, we usually know the consequences with some uncertainty,and there are also numerous constraints that needs to be taken into account.The presence of uncertainty and constraints makes decision making challenging.To resolve these challenges, we need to go beyond simple optimization, we alsoneed to get a good understanding of how the corresponding systems and objectsoperate, a good understanding of why we observe what we observe - this will helpus better predict what will be the consequences of different decisions. All theseproblems - in relation to different application areas - are the main focus of thisbook.…

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