English 2009 ISBN: 776 pages PDF 6,27 Mb Probability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling. 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 large numbers.

Discrete and continuous-time Markov chains are analyzed from a theoretical and computational point of view. Topics include the Chapman-Kolmogorov equations; irreducibility; the potential, fundamental, and reachability matrices; random walk problems; reversibility; renewal processes; and the numerical computation of stationary and transient distributions.

Probability markov chains queues and. Probability Markov Chains. Markov Chains, Queues, and Simulation. The Mathematical Basis of Performance Modeling. Probability, Markov Chains, Queues. 2 Continuous Probability Densities 41 2.1 Simulation of Continuous Probabilities. The Mathematical Basis of Performance. Markov Chains Chapter 5. The Exponential Distribution and the Poisson Process Chapter 6. Continuous-Time Markov Chains Chapter 7. Renewal Theory and Its Applications Chapter 8. Queueing Theory Chapter 9. Reliability Theory Chapter 10. Brownian Motion and Stationary Processes Chapter 11. Simulation Appendix:.

The M/M/1 queue and its extensions to more general birth-death processes are analyzed in detail, as are queues with phase-type arrival and service processes. The M/G/1 and G/M/1 queues are solved using embedded Markov chains; the busy period, residual service time, and priority scheduling are treated. Open and closed queueing networks are analyzed.

The final part of the book addresses the mathematical basis of simulation. Each chapter of the textbook concludes with an extensive set of exercises. An instructor's solution manual, in which all exercises are completely worked out, is also available (to professors only). Numerous examples illuminate the mathematical theories Carefully detailed explanations of mathematical derivations guarantee a valuable pedagogical approach Each chapter concludes with an extensive set of exercises Download.

Lecture schedule Date Notes Sessions Expected Topics to be Covered Sept. 7/11 Introduction and review of networking concepts Sept. 11/14/18 1.1-1.23 [] Review of probability theory, random variables, CDF, PDF, PMF, and some common distributions Sept. 21 1.24-1.30, 2.1-2.4 Discrete RVs, Random processes, Markov processes Sept. 25 No lecture on Sept.

25, as it will be used for poster presentations on Nov. 28 3.1-3.5 [] Markov chains Oct. 2 3.6-3.13 Markov matrices Assignment 1 due date: Oct. 5 4.1-4.12 [] Steady state analysis of Markov chains Steady state analysis using difference equations.

Google page rank. 9 is Thanksgiving day. For all students: Email me your selected topic(s), on/before Nov.

16 7.1-7.6 [] Queuing analysis, M/M/1, M/M/1/B queues Due date: Oct. 19 Midterm Exam (during lecture time) [] Oct.

23/26 8.1, 8.2, 8.3 [] Leaky bucket and token bucket Oct. 2 9.1-9.4 [] Error Control Protocols, SW ARQ, GBN ARQ, SR ARQ Nov. 6 10.1-10.4 [] MAC Protocols, Pure ALOHA, Slotted ALOHA, Stability issue of ALOHA Nov. 9 10.5, 10.6 [] CSMA/CD, CSMA/CA Due date: Nov. 16 IEEE 802.11 Nov. 20 11.1, 11.2 [] [] Modeling Network Traffic, Poisson Process Nov. 23 11.3, 11.5-11.8, 11.12, 11.13 Realistic models for Poisson traffic, Self-similar traffic, On-Off source/ Transmission error model Due date: Nov.

27 Review Nov. Sade Be That Easy Mp3 Download. 30 FUN Workshop, Nov.