NEWS

- PaCal 1.5 released

Wed, 10 Jul 2013

* Initial implementation of arithmetic of dependent random variables (Bayesian network style).

* Parallel computation (multicore). Set`params.general.parallel=True`

to enable. - PaCal 1.5beta released

Thu, 7 Jun 2012

* beta version of arithmetic of dependent random variables (Bayesian network style)

* parts not related to dependent variables are as stable as in previous releases, so it is safe to use 1.5beta as default - PaCal 1.1 released

Mon, 28 Nov 2011

* operations on two dependent random variables

* joint distribution models: normal, copulas

* joint distribution of two order statistics

* PaCal now depends on sympy - PaCal 1.0 released

Sun, 13 Nov 2011

New in version 1.0:

* order statistics

* noncentral distributions: Chi^2, F, Beta and T

* optimized routines for sum, average, max, min etc. of i.i.d. random variables

* Extreme Value Distributions: Gumbel, Weibull, Frechet

* better accuracy for Beta distribution

* methods interp_error and interp_error_by_segment return estimates of

interpolation errors

* a warning is issued when dependent RVs are used in operations

(use params.general.warn_on_dependent = False to suppress)

* bug fixes

**What is PaCAL?** PaCAL is a Python package which allows you to
perform arithmetic on random variables just like you do with ordinary
program variables. The variables can follow practically any distribution.
Below are some examples which explain what the project is all about.
See also our gallery.

**Who's behind it?**
PaCAL is maintained by Szymon Jaroszewicz and Marcin Korzeń

**How do I get started?**
Requirements and downloading information can be found
in getting started.

**How does it work?**
Discussion of the implementation and some theoretical guarantees are in this paper:

Szymon Jaroszewicz and Marcin Korzeń. "Arithmetic Operations on Independent Random Variables: A Numerical Approach", SIAM Journal on Scientific Computing, Volume 34, Issue 3, 2012, pages A1241-A1265.

The paper is available directly from SIAM or as a preprint.

**Acknowledgements.**
PaCAL was supported by Research Grant no. 0685/B/T02/2009/37 of
the Polish Ministry of Science and Higher Education (Ministerstwo
Nauki i Szkolnictwa Wyższego).
An inspiration for PaCAL was the Chebfun project.

from pacal import * dL = UniformDistr(1,3) L0 = UniformDistr(9,11) dT = NormalDistr(1,1) K = dL / (L0 * dT) K.plot() show()will produce the following chart:

This example corresponds to measuring the coefficient of thermal expansion

Y = UniformDistr(1,2) X = UniformDistr(3,4) A = atan(Y / X) A.plot() show()will show

This example corresponds to measuring the angle