PaCAL: A Python Package for Arithmetic Computations with Random Variables

Marcin Korzeń and Szymon Jaroszewicz

Journal of Statistical Software, 2014, vol. 057, issue i10

Abstract: In this paper we present PaCAL, a Python package for arithmetical computations on random variables. The package is capable of performing the four arithmetic operations: addition, subtraction, multiplication and division, as well as computing many standard functions of random variables. Summary statistics, random number generation, plots, and histograms of the resulting distributions can easily be obtained and distribution parameter fitting is also available. The operations are performed numerically and their results interpolated allowing for arbitrary arithmetic operations on random variables following practically any probability distribution encountered in practice. The package is easy to use, as operations on random variables are performed just as they are on standard Python variables. Independence of random variables is, by default, assumed on each step but some computations on dependent random variables are also possible. We demonstrate on several examples that the results are very accurate, often close to machine precision. Practical applications include statistics, physical measurements or estimation of error distributions in scientific computations.

Date: 2014-05-06
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v057i10/v57i10.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... i10/PaCal-1.5.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... 057i10/v57i10.py.zip
https://www.jstatsoft.org/index.php/jss/article/do ... 7i10-replication.txt

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:057:i10

DOI: 10.18637/jss.v057.i10

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().