Mathematical Models of Information and Stochastic Systems (eBook, PDF)
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This text shows that the amount of knowledge about a system plays an important role in the mathematical models used to foretell the future of the system. It explains how to derive probability distributions to predict the behavior of systems based on what is known about the system. The author develops probability theory from a few basic concepts, explores the relationship between probability and time, and describes the bit error rate with examples-a detail not found in many other probability books. Drawing on many disciplines that include physics, engineering, economics, and biology, the text c...
This text shows that the amount of knowledge about a system plays an important role in the mathematical models used to foretell the future of the system. It explains how to derive probability distributions to predict the behavior of systems based on what is known about the system. The author develops probability theory from a few basic concepts, explores the relationship between probability and time, and describes the bit error rate with examples-a detail not found in many other probability books. Drawing on many disciplines that include physics, engineering, economics, and biology, the text contains numerous case studies, examples, tables, and problems. A solutions manual is available for qualifying instructors.
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