To install click the Add extension button. That's it.

The source code for the WIKI 2 extension is being checked by specialists of the Mozilla Foundation, Google, and Apple. You could also do it yourself at any point in time.

4,5
Kelly Slayton
Congratulations on this excellent venture… what a great idea!
Alexander Grigorievskiy
I use WIKI 2 every day and almost forgot how the original Wikipedia looks like.
Live Statistics
English Articles
Improved in 24 Hours
Languages
Recent
Show all languages
What we do. Every page goes through several hundred of perfecting techniques; in live mode. Quite the same Wikipedia. Just better.
.
Leo
Newton
Brights
Milds

# Computational statistics

Students working in the Statistics Machine Room of the London School of Economics in 1964.

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]

As in traditional statistics the goal is to transform raw data into knowledge,[2] but the focus lies on computer intensive statistical methods, such as cases with very large sample size and non-homogeneous data sets.[2]

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].[3]

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.

• 1/3
Views:
6 520
1 838
1 281
• ✪ MSc Computational Statistics & Machine Learning -- Dr David Barber
• ✪ Computational Statistics - Class #1
• ✪ Professor Gareth Roberts: "New challenges in Computational Statistics"

## References

1. ^ Nolan, D. & Temple Lang, D. (2010). "Computing in the Statistics Curricula", The American Statistician 64 (2), pp.97-107.
2. ^ a b Wegman, Edward J. “Computational Statistics: A New Agenda for Statistical Theory and Practice.Journal of the Washington Academy of Sciences, vol. 78, no. 4, 1988, pp. 310–322. JSTOR
3. ^ Lauro, Carlo (1996), "Computational statistics or statistical computing, is that the question?", Computational Statistics & Data Analysis, 23 (1): 191–193, doi:10.1016/0167-9473(96)88920-1

### Articles

• Albert, J.H.; Gentle, J.E. (2004), Albert, James H; Gentle, James E (eds.), "Special Section: Teaching Computational Statistics", The American Statistician, 58: 1–1, doi:10.1198/0003130042872
• Wilkinson, Leland (2008), "The Future of Statistical Computing (with discussion)", Technometrics, 50 (4): 418–435, doi:10.1198/004017008000000460

### 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
• Gentle, James E. (2002), Elements of Computational Statistics, Springer, ISBN 0-387-95489-9
• Gentle, James E.; Härdle, Wolfgang; Mori, Yuichi, eds. (2004), Handbook of Computational Statistics: Concepts and Methods, Springer, ISBN 3-540-40464-3
• Givens, Geof H.; Hoeting, Jennifer A. (2005), Computational Statistics, Wiley Series in Probability and Statistics, Wiley-Interscience, ISBN 978-0-471-46124-1
• Klemens, Ben (2008), Modeling with Data: Tools and Techniques for Statistical Computing, Princeton University Press, ISBN 978-0-691-13314-0
• Monahan, John (2001), Numerical Methods of Statistics, Cambridge University Press, ISBN 978-0-521-79168-7
• Rose, Colin; Smith, Murray D. (2002), Mathematical Statistics with Mathematica, Springer Texts in Statistics, Springer, ISBN 0-387-95234-9
• Thisted, Ronald Aaron (1988), Elements of Statistical Computing: Numerical Computation, CRC Press, ISBN 0-412-01371-1
• Gharieb, Reda. R. (2017), Data Science: Scientific and Statistical Computing, Noor Publishing, ISBN 978-3-330-97256-8
Basis of this page is in Wikipedia. Text is available under the CC BY-SA 3.0 Unported License. Non-text media are available under their specified licenses. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc. WIKI 2 is an independent company and has no affiliation with Wikimedia Foundation.