# Computational statistics

File:London School of Economics Statistics Machine Room 1964.jpg
Statistics algorithms were one of the first uses of modern computers.

Computational statistics, or statistical computing, is the interface between statistics and computer science. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics. This area is also developing rapidly, leading to calls that a broader concept of computing should be taught as part of general statistical education.[1]

The terms 'computational statistics' and 'statistical computing' are often used interchangeably, although Carlo Lauro (a former president of the International Association for Statistical Computing) proposed making a distinction, defining 'statistical computing' as "the application of computer science to statistics", and 'computational statistics' as "aiming at the design of algorithm for implementing statistical methods on computers, including the ones unthinkable before the computer age (e.g. bootstrap, simulation), as well as to cope with analytically intractable problems" [sic].[2]

The term 'Computational statistics' may also be used to refer to computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation, artificial neural networks and generalized additive models.

## References

1. Nolan, D. & Temple Lang, D. (2010). "Computing in the Statistics Curricula", The American Statistician 64 (2), pp.97-107.
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### Articles

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### Books

• Drew, John H.; Evans, Diane L.; Glen, Andrew G.; Lemis, Lawrence M. (2007), Computational Probability: Algorithms and Applications in the Mathematical Sciences, Springer International Series in Operations Research & Management Science, Springer, ISBN 0-387-74675-7<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
• Gentle, James E. (2002), Elements of Computational Statistics, Springer, ISBN 0-387-95489-9<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
• Gentle, James E.; Härdle, Wolfgang; Mori, Yuichi, eds. (2004), Handbook of Computational Statistics: Concepts and Methods, Springer, ISBN 3-540-40464-3<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
• Givens, Geof H.; Hoeting, Jennifer A. (2005), Computational Statistics, Wiley Series in Probability and Statistics, Wiley-Interscience, ISBN 978-0-471-46124-1<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
• Klemens, Ben (2008), Modeling with Data: Tools and Techniques for Statistical Computing, Princeton University Press, ISBN 978-0-691-13314-0<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
• Monahan, John (2001), Numerical Methods of Statistics, Cambridge University Press, ISBN 978-0-521-79168-7<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
• Rose, Colin; Smith, Murray D. (2002), Mathematical Statistics with Mathematica, Springer Texts in Statistics, Springer, ISBN 0-387-95234-9<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
• Thisted, Ronald Aaron (1988), Elements of Statistical Computing: Numerical Computation, CRC Press, ISBN 0-412-01371-1<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>