Applied Probability and Stochastic Processes

Applied Probability and Stochastic Processes is an edited work written in honor of Julien Keilson. This volume has attracted a host of scholars in applied probability, who have made major contributions to the field, and have written survey and state-of-the-art papers on a variety of applied probabil...

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Bibliographic Details
Other Authors: Shanthikumar, J. George (Editor), Sumita, Ushio (Editor)
Format: eBook
Language:English
Published: New York, NY Springer US 1999, 1999
Edition:1st ed. 1999
Series:International Series in Operations Research & Management Science
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1 Comments on the Perturbation Method
  • 2 Some Aspects of Complete Monotonicity in Time-Reversible Markov Chains
  • 3 Transformations of Poisson Processes: Particle Systems and Networks
  • 4 On the Local Time of the Brownian Bridge
  • 5 Probabilistic Token Causation: A Bayesian Perspective
  • 6 On a Statistical Algorithm to Decode Heavily Corrupted Linear Codes
  • 7 Mean Cover Times for Coupon Collectors and Star Graphs
  • 8 Models for the Spread of Infection via Pairing at Parties
  • 9 Extremes of Random Numbers of Random Variables: A Survey
  • 10 Optimality of Sequential Quality Control via Stochastic Orders
  • 11 Reallocatable GSMP with Sequentially Dependent Lifetimes: Clockwise Decomposability and Its Applications
  • 12 Random Matrices and the Number of {0,1} Matrices with Given Row and Column Sums
  • 13 Monotone Optimal Policies for Left-Skip-Free Markov Decision Processes
  • 14 Optimal Routing Control in Retrial Queues
  • 15 Waiting Times when Service Times are Stable Laws: Tamed and Wild
  • 16 Winning the Hand of the Princess Saralinda
  • 17 Analysis of Multiple Queues with Passing Servers
  • 18 Some Properties of Throughput in a Queueing Network with Changing-Speed Servers and Blocking
  • 19 Quasi-stationary Distributions of Markov Chains Arising from Queueing Processes: A survey
  • 20 Estimating Customer Loss Rates From Transactional Data