The Monte Carlo Method

  • Christiane Lemieux
Part of the Springer Series in Statistics book series (SSS)
The Monte Carlo method is a widely used tool in many disciplines, including physics, chemistry, engineering, finance, biology, computer graphics, operations research, and management science. Examples of problems that it can address are:
  • A call center manager wants to know if adding a certain number of service representatives during peak hours would help decrease the waiting time of calling customers.

  • A portfolio manager needs to determine the magnitude of the loss in value that could occur with a 1% probability over a one-week period.

  • The designer of a telecommunications network needs to make sure that the probability of losing information cells in the network is below a certain threshold.

Realistic models of the systems above typically assume that at least some of their components behave in a random way. For instance, the call arrival times and processing times for the call center cannot realistically be assumed to be fixed and known ahead of time, and thus it makes sense instead to assume that they occur according to some stochastic model.


Monte Carlo Method Cumulative Distribution Function Trapezoidal Rule Monte Carlo Estimator Interarrival Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  1. 1.University of WaterlooDept. Statistics & Actuarial ScienceWaterlooCanada

Personalised recommendations