randproc: Data structures and support functions for working with random processes

[ bsd3, data-structures, library ] [ Propose Tags ] [ Report a vulnerability ]

RandProc.hs - a Haskell library for working with random processes in a mathematically rigorous way (Concepts taken from Random Processes - a Mathematical Approach for Engineers by: - Robert M. Gray - Lee D. Davisson Prentice-Hall Information and System Sciences Series, Thomas Kailath, Series Editor)


[Skip to Readme]

Modules

[Index]

Downloads

Maintainer's Corner

Package maintainers

For package maintainers and hackage trustees

Candidates

  • No Candidates
Versions [RSS] 0.1, 0.2, 0.3, 0.4
Dependencies base (>=3 && <5) [details]
License BSD-3-Clause
Author David Banas
Maintainer dbanas@banasfamily.net
Category Data Structures
Home page http://www.haskell.org/haskellwiki/Random_Processes
Bug tracker http://trac.haskell.org/RandProc/
Uploaded by DavidBanas at 2011-07-03T14:57:29Z
Distributions NixOS:0.4
Reverse Dependencies 1 direct, 0 indirect [details]
Downloads 3292 total (19 in the last 30 days)
Rating (no votes yet) [estimated by Bayesian average]
Your Rating
  • λ
  • λ
  • λ
Status Docs uploaded by user
Build status unknown [no reports yet]

Readme for randproc-0.4

[back to package description]
This is the `RandProc` library, written by David Banas.

For more info, please, see the comments in `Data/RandProc.hs`.
Alternatively, view the Haddock generated HTML documentation:
  $ cabal haddock

For information on the community support infrastructure for this library,
view the home page for this library:
  http://www.haskell.org/haskellwiki/Random_Processes
There you will find links to:
- mailing list
- blog
- bug tracker
- etc.

For usage examples, please, see:
- test/Test.hs
    Darcs check-in tests
- Data/RandProc/Examples/Problems/*.hs
    example problems, taken from the text upon which this library is based

The intent of this library is to provide the data structures and support
functions necessary for working with abstract probability spaces, in a
mathematically rigorous way. It is not intended to provide functionality for
generating distributions, moments, expectations, etc., as there are already
libraries available for this. (`Probabilistic Functional Programming` is one
that I've seen, but haven't had a chance to investigate, yet.)