• Publications
• Influence
Stochastic Processes
• S. Ross
• Computer Science
• 6 December 1982
• 5,775
• 342
• PDF
Introduction to Probability Models.
• Computer Science
• 1 November 1975
• 2,940
• 253
• PDF
Introduction to Probability Models (4th ed.).
• Computer Science
• 1 June 1990
• 879
• 97
Introduction to Probability Models, Eighth Edition
introduction to probability models eighth edition is available in our book collection an online access to it is set as public so you can get it instantly. Expand
• 693
• 35
A Sequential Stochastic Assignment Problem
• Mathematics
• 1 March 1972
Suppose there are n men available to perform n jobs. The n jobs occur in sequential order with the value of each job being a random variable X. Associated with each man is a probability p. If a "p"Expand
• 165
• 28
An Optimal Algorithm for Monte Carlo Estimation
• Computer Science, Mathematics
• SIAM J. Comput.
• 1 March 2000
A typical approach to estimate an unknown quantity $\mu$ is to design an experiment that produces a random variable Z, distributed in [0,1] with E[Z]=\mu\$, run this experiment independently a number of times, and use the average of the outcomes as the estimate. Expand
• 143
• 19
An optimal algorithm for Monte Carlo estimation
• Computer Science
• Proceedings of IEEE 36th Annual Foundations of…
• 23 October 1995
A typical approach to estimate an unknown quantity /spl mu/ is to design an experiment that produces a random variable Z distributed in [O,1] with E[Z]=/spl mu/, run this experiment independently a number of times and use the average of the outcomes as the estimate. Expand
• 102
• 19
Quality Control under Markovian Deterioration
We suppose that a production process may be in any one of a countable number of states and that the quality of the item produced is a function of this underlying state. It is also supposed that theExpand
• 193
• 13
• PDF
A First Course in Probability
1. Combinatorial Analysis. 2. Axioms of Probability. 3. Conditional Probability and Independence. 4. Random Variables. 5. Continuous Random Variables. 6. Jointly Distributed Random Variables. 7.Expand
• 532
• 11
A Course in Simulation
• 201
• 9