Inbunden, Engelska, 2014
2 855 kr
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7 produkter
7 produkter
- Inbunden, Engelska, 2022
1 709 kr
Skickas inom 10-15 vardagar
This book is about methodological aspects of uncertainty propagation in data processing. Uncertainty propagation is an important problem: while computer algorithms efficiently process data related to many aspects of their lives, most of these algorithms implicitly assume that the numbers they process are exact. In reality, these numbers come from measurements, and measurements are never 100% exact. Because of this, it makes no sense to translate 61 kg into pounds and get the result—as computers do—with 13 digit accuracy.In many cases—e.g., in celestial mechanics—the state of a system can be described by a few numbers: the values of the corresponding physical quantities. In such cases, for each of these quantities, we know (at least) the upper bound on the measurement error. This bound is either provided by the manufacturer of the measuring instrument—or is estimated by the user who calibrates this instrument. However, in many other cases, the description of the system is more complex than a few numbers: we need a function to describe a physical field (e.g., electromagnetic field); we need a vector in Hilbert space to describe a quantum state; we need a pseudo-Riemannian space to describe the physical space-time, etc.To describe and process uncertainty in all such cases, this book proposes a general methodology—a methodology that includes intervals as a particular case. The book is recommended to students and researchers interested in challenging aspects of uncertainty analysis and to practitioners who need to handle uncertainty in such unusual situations. - E-bokEngelska, 2022
2 105 kr
Läs direkt efter köp
This book is about methodological aspects of uncertainty propagation in data processing. Uncertainty propagation is an important problem: while computer algorithms efficiently process data related to many aspects of their lives, most of these algorithms implicitly assume that the numbers they process are exact. In reality, these numbers come from measurements, and measurements are never 100% exact. Because of this, it makes no sense to translate 61 kg into pounds and get the result—as computers do—with 13 digit accuracy.
In many cases—e.g., in celestial mechanics—the state of a system can be described by a few numbers: the values of the corresponding physical quantities. In such cases, for each of these quantities, we know (at least) the upper bound on the measurement error. This bound is either provided by the manufacturer of the measuring instrument—or is estimated by the user who calibrates this instrument. However, in many other cases, the description of the system is more complex than a few numbers: we need a function to describe a physical field (e.g., electromagnetic field); we need a vector in Hilbert space to describe a quantum state; we need a pseudo-Riemannian space to describe the physical space-time, etc.
To describe and process uncertainty in all such cases, this book proposes a general methodology—a methodology that includes intervals as a particular case. The book is recommended to students and researchers interested in challenging aspects of uncertainty analysis and to practitioners who need to handle uncertainty in such unusual situations.
646 kr
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- Häftad, Engelska, 2023
1 709 kr
Skickas inom 10-15 vardagar
This book is about methodological aspects of uncertainty propagation in data processing. Uncertainty propagation is an important problem: while computer algorithms efficiently process data related to many aspects of their lives, most of these algorithms implicitly assume that the numbers they process are exact. In reality, these numbers come from measurements, and measurements are never 100% exact. Because of this, it makes no sense to translate 61 kg into pounds and get the result—as computers do—with 13 digit accuracy.In many cases—e.g., in celestial mechanics—the state of a system can be described by a few numbers: the values of the corresponding physical quantities. In such cases, for each of these quantities, we know (at least) the upper bound on the measurement error. This bound is either provided by the manufacturer of the measuring instrument—or is estimated by the user who calibrates this instrument. However, in many other cases, the description of the system is more complex than a few numbers: we need a function to describe a physical field (e.g., electromagnetic field); we need a vector in Hilbert space to describe a quantum state; we need a pseudo-Riemannian space to describe the physical space-time, etc.To describe and process uncertainty in all such cases, this book proposes a general methodology—a methodology that includes intervals as a particular case. The book is recommended to students and researchers interested in challenging aspects of uncertainty analysis and to practitioners who need to handle uncertainty in such unusual situations. - Del 10798 - Lecture Notes in Computer Science
Multi-Agent Based Simulation XVIII
International Workshop, MABS 2017, São Paulo, Brazil, May 8-12, 2017, Revised Selected Papers
Häftad, Engelska, 2018574 kr
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This book constitutes the thoroughly refereed post-conference proceedings of the 18th International Workshop on Multi-Agent-Based Simulation, MABS 2017, held in Sao Paulo, Brazil, in May 2017. The workshop was held in conjunction with the 16th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2017.The 15 revised full papers included in this volume were carefully selected from 23 submissions. The topic of the papers is about applying agent-based simulation techniques to real-world problems focusing on the confluence of socio-technical-natural sciences and multi-agent systems with a strong application/empirical vein. - E-bokEngelska, 2018
734 kr
Läs direkt efter köp
This book constitutes the thoroughly refereed post-conference proceedings of the 18th International Workshop on Multi-Agent-Based Simulation, MABS 2017, held in Sao Paulo, Brazil, in May 2017. The workshop was held in conjunction with the 16th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2017.
The 15 revised full papers included in this volume were carefully selected from 23 submissions. The topic of the papers is about applying agent-based simulation techniques to real-world problems focusing on the confluence of socio-technical-natural sciences and multi-agent systems with a strong application/empirical vein.
