PaCAL: A Python Package for Arithmetic Computations with Random Variables

By Marcin Korzen, Szymon Jaroszewicz. 2014

16 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Consistent Kernel Mean Estimation for Functions of Random Variables (2016, background): “For real valued distributions, integral transforms (Springer, 1979), mixtures of Gaussians (Milios, 2009), Laguerre polynomials (Williamson, 1989), and Chebyshev polynomials (Korzeń and Jaroszewicz, 2014) were proposed as convenient representations for numerical computation.”