Poisson processWiener processprobability model-buildingstochastic processes
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Abstract (AI)
Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory. The treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models, and it develops the methods of probability model-building.Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine
Key Findings
1
Chapter 1 precisely defines random variables and stochastic processes and introduces Wiener and Poisson processes.
2
It uses empirical examples to demonstrate the broad range of phenomena that can be modeled with stochastic processes.
3
The abstract indicates that later chapters continue examining additional stochastic-process topics, though their specific contents are not provided.
4
The text develops methods for constructing probability models rather than focusing solely on formal definitions.
5
The work provides an accessible introduction to stochastic processes and related mathematics for advanced undergraduate mathematics students.
Research Object
stochastic processes
Research Subject
mathematical modeling of empirical phenomena using probability-model building methods
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Publication Date
1999-01-01
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