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Modern Issues and Methods in BiostatisticsMonte Carlo Simulation

Modern Issues and Methods in Biostatistics: Monte Carlo Simulation [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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$U\left (0,1\right )$$ \end{document}. 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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Modern Issues and Methods in BiostatisticsMonte Carlo Simulation

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Publisher
Springer New York
Copyright
© Springer Science+Business Media, LLC 2011
ISBN
978-1-4419-9841-5
Pages
233 –259
DOI
10.1007/978-1-4419-9842-2_9
Publisher site
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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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$U\left (0,1\right )$$ \end{document}. 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.]

Published: Jun 16, 2011

Keywords: PBPK Model; Adaptive Design; Conditional Power; Druglike Molecule; Urea Cycle Disorder

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