Frank Beichelt, L. Paul Fatti
This book introduces stochastic processes and their applications for students in engineering, industrial statistics, science, operations research, business, and finance. It provides the theoretical foundations for modeling time-dependent random phenomena encountered in these disciplines. Through numerous science and engineering-based examples and exercises, the author presents the subject in a comprehensible, practically oriented way, but he also includes some important proofs and theoretically challenging examples and exercises that will appeal to more mathematically minded readers. Solutions to most of the exercises are included either in an appendix or within the text.
Probability Theory. Stochastic Processes. Poisson Processes. Renewal Processes. Discrete-Time Markov Chains. Continuous-Time Markov Chains. Wiener Processes. Spectral Analysis of Stationary Processes.
"The book's primary strength lies in its crisp and lively presentation of the subject with neither undue attention nor neglect for technical detail. ... The book is well prepared an accurate. ... [The] chapters on probability and general stochastic processes serve to homogenize the audience as well as make the text feel more self-contained. ... Is it worth owning? Yes ... . "
- The American Statistician, Aug. 2004, Vol. 58, No. 3
"The book's primary strength lies in its crisp and lively presentation of the subject with neither undue attention nor neglect for technical detail. ... The book is well prepared an accurate. ... [The] chapters on probability and general stochastic processes serve to homogenize the audience as well as make the text feel more self-contained. ... Is it worth owning? Yes ... . "- The American Statistician, Aug. 2004, Vol. 58, No. 3
Assuming basic knowledge of calculus and probability theory, this text presents stochastic processes in a user-friendly way, containing numerous worked examples. The large number of exercises allows readers to check their understanding of the underlying theory, along with their ability to apply stochastic modelling in their own fields.
Inhaltsverzeichnis
Probability Theory. Stochastic Processes. Poisson Processes. Renewal Processes. Discrete-Time Markov Chains. Continuous-Time Markov Chains. Wiener Processes. Spectral Analysis of Stationary Processes.
Klappentext
Assuming basic knowledge of calculus and probability theory, this text presents stochastic processes in a user-friendly way, containing numerous worked examples. The large number of exercises allows readers to check their understanding of the underlying theory, along with their ability to apply stochastic modelling in their own fields.
