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Sunday, 21 April 2013

Probability, Markov Chains, Queues, and Simulation

Posted on 15:49 by Unknown
Probability, Markov Chains, Queues, and Simulation
Author: William J. Stewart
Edition:
Binding: Hardcover
ISBN: 0691140626
Publisher: Princeton University Press

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Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling



Probability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling.
Probability, Markov Chains, Queues, and Simulation : The Mathematical Basis of Performance Modeling, ISBN-13: 9780691140629, ISBN-10: 0691140626
The detailed explanations of mathematical derivations and numerous illustrative examples make this textbook readily accessible to graduate and advanced undergraduate students taking courses in which stochastic processes play a fundamental role. The textbook is relevant to a wide variety of fields, including computer science, engineering, operations research, statistics, and mathematics. The textbook looks at the fundamentals of probability theory, from the basic concepts of set-based probability, through probability distributions, to bounds, limit theorems, and the laws of l

Markov Chains (Cambridge Series in Statistical and Probabilistic Mathematics)


In this rigorous account the author studies both discrete-time and continuous-time chains. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials, in th

Doing Bayesian Data Analysis: A Tutorial with R and BUGS


There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian

Fundamentals of Queueing Theory (Wiley Series in Probability and Statistics)


Praise for the Third Edition"This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . so

An Introduction to Markov Processes (Graduate Texts in Mathematics)


Provides a more accessible introduction than other books on Markov processes by emphasizing the structure of the subject and avoiding sophisticated measure theory Leads the reader to a rigorous unders

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)


Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a g

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