Stochastic Point Processes: Statistical Analysis, Theory, And Applications
Condition: SECONDHAND
This is a secondhand book. The jacket image is a photograph of the exact copy we have in stock. This image shows the condition of this book. Further condition remarks are below.
Condition remarks:
Book: Good
Jacket: Worn/faded, no tears
Pages: Good
Markings: Previous owner
A rigorous academic text in the field of applied probability and statistics, Stochastic Point Processes: Statistical Analysis, Theory, and Applications presents a comprehensive treatment of the mathematical framework governing random point processes and their real-world utility. The work details the statistical analysis of event-driven data, covering topics such as renewal processes, Poisson processes, and spectral analysis, while grounding each concept in both theoretical rigor and practical methodology. Written with the precision and authority expected of advanced graduate-level scholarship, it instructs readers in the techniques necessary to model and analyze sequences of random events arising in fields ranging from telecommunications to reliability engineering. The text illustrates how stochastic process theory bridges abstract probability with empirical data analysis, making it an indispensable reference for statisticians, engineers, and applied mathematicians alike.
Author: Peter A. W. Lewis
Format: Hardback
Genre: Mathematics
Condition remarks:
Book: Good
Jacket: Worn/faded, no tears
Pages: Good
Markings: Previous owner
A rigorous academic text in the field of applied probability and statistics, Stochastic Point Processes: Statistical Analysis, Theory, and Applications presents a comprehensive treatment of the mathematical framework governing random point processes and their real-world utility. The work details the statistical analysis of event-driven data, covering topics such as renewal processes, Poisson processes, and spectral analysis, while grounding each concept in both theoretical rigor and practical methodology. Written with the precision and authority expected of advanced graduate-level scholarship, it instructs readers in the techniques necessary to model and analyze sequences of random events arising in fields ranging from telecommunications to reliability engineering. The text illustrates how stochastic process theory bridges abstract probability with empirical data analysis, making it an indispensable reference for statisticians, engineers, and applied mathematicians alike.