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- Publisher: Engineering Science Reference
- ISBN-10: 1522550453
- ISBN-13: 9781522550457
- Format: 21.6 x 27.9 x 1.8 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
Stochastic Methods for Estimation and Problem Solving in Engineering (e-book) (used book) | bookbook.eu
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Utilizing mathematical algorithms is an important aspect of recreating real-world problems in order to make important decisions. By generating a randomized algorithm that produces statistical patterns, it becomes easier to find solutions to countless situations. Stochastic Methods for Estimation and Problem Solving in Engineering provides emerging research on the role of random probability systems in mathematical models used in various fields of research. While highlighting topics, such as random probability distribution, linear systems, and transport profiling, this book explores the use and behavior of uncertain probability methods in business and science. This book is an important resource for engineers, researchers, students, professionals, and practitioners seeking current research on the challenges and opportunities of non-deterministic probability models.
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- Publisher: Engineering Science Reference
- ISBN-10: 1522550453
- ISBN-13: 9781522550457
- Format: 21.6 x 27.9 x 1.8 cm, hardcover
- Language: English English
Utilizing mathematical algorithms is an important aspect of recreating real-world problems in order to make important decisions. By generating a randomized algorithm that produces statistical patterns, it becomes easier to find solutions to countless situations. Stochastic Methods for Estimation and Problem Solving in Engineering provides emerging research on the role of random probability systems in mathematical models used in various fields of research. While highlighting topics, such as random probability distribution, linear systems, and transport profiling, this book explores the use and behavior of uncertain probability methods in business and science. This book is an important resource for engineers, researchers, students, professionals, and practitioners seeking current research on the challenges and opportunities of non-deterministic probability models.
Reviews