Summary and Outlook

  • Luca Martino
  • David Luengo
  • Joaquín Míguez
Chapter
Part of the Statistics and Computing book series (SCO)

Abstract

In this monograph, we have described the theory and practice of pseudo-random variate generation. This is the core of Monte Carlo simulations and, hence, of practical importance for a large number of applications in various fields, including computational statistics, cryptography, computer modeling, games, etc. The focus has been placed on independent and exact sampling methods, as opposed to techniques that produce weighted (e.g., importance sampling) and/or correlated populations (e.g., MCMC).

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    F. Liang, C. Liu, R. Caroll, Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley Series in Computational Statistics (Wiley, London, 2010)Google Scholar
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    C.P. Robert, G. Casella, Monte Carlo Statistical Methods (Springer, New York, 2004)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Luca Martino
    • 1
  • David Luengo
    • 2
  • Joaquín Míguez
    • 1
  1. 1.Department of Signal Theory and CommunicationsCarlos III University of MadridMadridSpain
  2. 2.Department of Signal Theory and CommunicationsTechnical University of MadridMadridSpain

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