Basics of Applied Stochastic Processes (Probability and Its Applications) by Richard Serfozo (PDF)

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    Ebook Info

    • Published: 2009
    • Number of pages: 443 pages
    • Format: PDF
    • File Size: 3.79 MB
    • Authors: Richard Serfozo

    Description

    Stochastic processes are mathematical models of random phenomena that evolve according to prescribed dynamics. Processes commonly used in applications are Markov chains in discrete and continuous time, renewal and regenerative processes, Poisson processes, and Brownian motion. This volume gives an in-depth description of the structure and basic properties of these stochastic processes. A main focus is on equilibrium distributions, strong laws of large numbers, and ordinary and functional central limit theorems for cost and performance parameters. Although these results differ for various processes, they have a common trait of being limit theorems for processes with regenerative increments. Extensive examples and exercises show how to formulate stochastic models of systems as functions of a system’s data and dynamics, and how to represent and analyze cost and performance measures. Topics include stochastic networks, spatial and space-time Poisson processes, queueing, reversible processes, simulation, Brownian approximations, and varied Markovian models.The technical level of the volume is between that of introductory texts that focus on highlights of applied stochastic processes, and advanced texts that focus on theoretical aspects of processes.

    User’s Reviews

    Reviews from Amazon users which were colected at the time this book was published on the website:

    ⭐You can tell this is a first edition, it has a lot of errors (not just typos). What I wonder is if the author used the book enough with students before publishing it, because you can find errors in the exercises that can be easily spotted.It’s still a good book, but maybe it’s better to wait for the next edition (given the high price). You can go for Resnick’s (

    ⭐) classic book, which is pretty good.

    ⭐5 stars from an MIT math undergrad!This was a great supplement to the MIT stochastic processes course (which is quite applied) and had enough theory to leave me very satisfied.

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