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Statistical Computer Simulations And Monte Carlo Methods

Author

Listed:
  • Sanda Micula

    (Babeş-Bolyai University)

Abstract

In this paper we present Monte Carlo methods for estimating distribution characteristics of random variables. We describe algorithms for computer simulations of some common distributions and their implementation in MATLAB. The paper concludes with examples and applications.

Suggested Citation

  • Sanda Micula, 2015. "Statistical Computer Simulations And Monte Carlo Methods," Romanian Economic Business Review, Romanian-American University, vol. 9(2), pages 384-394, December.
    • Handle: RePEc:rau:journl:v:10:y:2015:i:2:p:384-394
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    File URL: http://www.rebe.rau.ro/RePEc/rau/jisomg/WI15/JISOM-WI15-A11.pdf
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    References listed on IDEAS

    as
    1. Yuguo Chen & Junyi Xie & Jun S. Liu, 2005. "Stopping‐time resampling for sequential Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 199-217, April.
    2. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    3. Yuguo Chen & Persi Diaconis & Susan P. Holmes & Jun S. Liu, 2005. "Sequential Monte Carlo Methods for Statistical Analysis of Tables," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 109-120, March.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Sanda Micula & Ioana D. Pop, 2016. "Simulations Of Continuous Random Variables And Monte Carlo Methods," Romanian Economic Business Review, Romanian-American University, vol. 10(2), pages 435-447, December.

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