Synonyms
In probability modeling: Monte Carlo procedures, Random sampling with pseudo-random numbers. In statistical inference: Bootstraps, (simulated) permutation tests, Markov chain Monte Carlo (MCMC)
Glossary
- Beta distributions:
-
The general density function is \( f\left(x,\mid, \alpha,, \beta \right)=\frac{\varGamma \left(\alpha +\beta \right)}{\varGamma \left(\alpha \right)\varGamma \left(\beta \right)}{x}^{\alpha -1}{\left(1-x\right)}^{\beta -1},\,\, \)
for 0 < x < 1 (0 otherwise). We denote a beta distribution by BETA(α, β). Shape parameters: α > 0 , β > 0. Note: \( \varGamma \left(1/2\right)=\sqrt{\pi } \) and, for positive integer k, Γ(k) = (k − 1)!
- Bootstraping:
-
A method of statistical inference based on extensive simulation, often used to make confidence intervals. Takes a large number of resamples from the original data (or from a distribution suggested by them) to assess variability. Introduced by Efron (1979) and discussed in Efron and Tibshirani (1998)
...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bain LJ, Engelhardt M (1973) Interval estimation for the two-parameter double exponential distribution. Technometrics 15:875–887
Basu D (1980) Randomization analysis of experimental data: the fisher randomization test. J Am Stat Assoc 75(371):575–582. JSTOR 2287648
Bayer D, Diaconis P (1992) Trailing the dovetail shuffle to its lair. Ann Appl Probab 2(2):294–313
Blitzstein JK, Hwang J (2015) Introduction to probability. CRC Press/Chapman Hall, New York
Box GEP, Muller ME (1958) A note on the generation of random normal deviates. Ann Math Stat 29(2):610–611
Braun WJ, Murdoch DJ (2007) A first course in statistical programming with R. Cambridge University Press, Cambridge
Chib S, Greenberg E (1994) Understanding the Metropolis-Hastings algorithm. Am Stat 49:327–335
Diaconis P, Holmes S, Montgomery R (2007) Dynamical bias in the coin toss. SIAM Rev 49(2):211–235. http://epubs.siam.org/doi/abs/10.1137/S0036144504446436?journalCode=siread
Dyson G (2012) Turing’s cathedral: the origins of the digital universe. Vintage Books, New York
Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7(1):126
Efron B, Tibshirani RJ (1998) An introduction to the bootstrap. Chapman & Hall/CRC, Boca Raton
Eudey TL, Kerr JD, Trumbo BE (2010) Using R to simulate permutation distributions for some elementary experimental designs. J Stat Educ 18(1). http://www.amstat.org/publications/jse/v18n1/eudey.pdf
Feller W (1957) Introcuction to probability theory and its applications, 2nd edn. Wiley, New York
Feynman RP (1988) What do you care what other people think? W W Norton, New York
Fisher RA (1935) The design of experiments. Oliver & Boyd, Edinburgh
Gentle J (1998) Random number generation and Monte Carlo methods. Springer, Berlin/Heidelberg/New York
Grolemund G, Wickham H (2014) Hands-on programming with R. O’Reilly Media, Sebastepol
Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1):97–109. JSTOR 2334940
Kapperman RF (1975) Conditional confidence intervals for double exponential distribution parameters. Technometrics 17:233–235
Kinney SK, Reiter JP, Reznek AP, Miranda J, Harmin R, Abowd JM (2013) Toward unrestricted public use of business microdata: The Longitudinal Business Database. Int Stat Rev 79(3):362–384
Lewis PAW (1986) Graphical analysis of some pseud0-random number generators. Technical report NP-55586025, US Naval Postgraduate School, Monterrey
Liu JS (2004) Monte Carlo strategies in scientific computing. Springer, Berlin/Heidelberg/New York
Lunneborg CE (1999) Data analysis by resampling: concepts and applications. Duxbury, Pacific Grove
Marsaglia G (1995) The Marsaglie random number CDROM, including the Diehard battery of tests of randomness. Department of Statistics, Florida State U. http://stat.fsu.edu/pub/diehard/
Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudorandom number generator. J ACM Trans Model Comput Simul (TOMACS) 8(1):3–30
Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equations of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092
von Neumann J (1951) Various techniques used in connection with random digits. Natl Bur Stan Appl Math Ser 12:3638. https://dornsifecms.usc.edu/assets/sites/520/docs/VonNeumann-ams12p36-38.pdf
Ott RL, Longnecker M (2016) Introdution to statistical methods and data analysis, 7th edn. Cengage, Boston
RAND Corporation (1955) A million random digits with 100,000 normal deviates. Free Press, Glencoe
Ransey FL, Schafer DW (2002) The statistical sleuth: a course in methods of data analysis, 2nd edn. Duxbury, Pacific Grove
Ross SM (1997) Introduction to probability models, 6th edn. Academic Press, San Diego
Ruxton G (2006) The unequal variance t-test is an underused alternative to Student’s t-test and the MannWhitney U test. Behav Ecol 17:688–690
Suess EA, Trumbo BE (2010) Introduction to probability simulation and Gibbs sampling with R. Springer, Berlin/Heidelberg/New York
Trumbo BE (2006, 2006) Random numbers from nonrandom arithmetic, STATS. J Am Stat Assoc (46):23–27. Alexandria
Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, Berlin/Heidelberg/New York
Welch BL (1947) The generalization of “Student’s” problem when several different population variances are involved. Biometrika 34(12):28–35
Wichura MS (1988) Algorithm AS 241: the percentage points of the normal distribution. J R Stat Soc Ser C 57(3):477–484
Yang Y (2012) Quantum computing and quantum information. Stat Sci 27(3):373–394
Acknowledgments
Several of the examples and figures here are adapted from material in the first three chapters of Suess and Trumbo (2010) and in Trumbo (2006). Our perspectives on simulation have been influenced by Braun and Murdoch (2007), Blitzstein and Hwang (2015), Grolemund and Wickham (2014), Gentle (1998), and Venables and Ripley (2002). We thank colleagues, current and former students, three anonymous referees, and our associate editor for useful suggestions.
The simulations in this article used R statistical software (open-source software available without cost from www.r-project.org for use on Windows, Macintosh, or UNIX operating systems). We hope our descriptions and examples of code are sufficiently clear that readers could repeat our simulations using other software. Python open-source software may be the most convenient alternative. Commercial software such as Mathematica, SAS, and Excel can also do simulations, but they may lack analogues of the specialized functions for probability and statistics we have used.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media LLC, part of Springer Nature
About this entry
Cite this entry
Trumbo, B.E., Suess, E.A. (2018). Simulations. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_110189
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7131-2_110189
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-7130-5
Online ISBN: 978-1-4939-7131-2
eBook Packages: Computer ScienceReference Module Computer Science and Engineering