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Monte Carlo Simulation

  • Mark Chang
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Monte Carlo simulation is the technique to mimic a dynamic system or process using a computer program. Computer simulations, as an efficient and effective research tool, have been used virtually everywhere, including in biostatistics, engineering, finance, and other areas. To perform simulations, we often need to draw random samples from a certain probability distribution. Typically, the simplest and most important random sampling procedure is sampling from the uniform distribution over (0, 1), denoted by \(U\left (0,1\right )\). The computer-generated “random” number is not truly random because the sequence of numbers is determined by the so-called seed, an initial number. Random variates from other distributions can often be obtained by applying a transformation to uniform variates. There are usually several algorithms available to generate random numbers from a particular distribution. The algorithms differ in speed, accuracy, and the computer memory required. Many software packages have implemented various algorithms to generate random numbers with different probability distributions. Here we introduce a few commonly used algorithms.

Keywords

PBPK Model Adaptive Design Conditional Power Druglike Molecule Urea Cycle Disorder 
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.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Mark Chang
    • 1
  1. 1.BiometricsAMAG Pharmaceuticals, Inc.LexingtonUSA

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