probability: Probabilistic Functional Programming

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The Library allows exact computation with discrete random variables in terms of their distributions by using a monad. The monad is similar to the List monad for non-deterministic computations, but extends the List monad by a measure of probability. Small interface to R plotting.

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Versions	0.1, 0.2, 0.2.1, 0.2.2, 0.2.2.1, 0.2.3, 0.2.3.1, 0.2.4, 0.2.4.1, 0.2.5, 0.2.5.1, 0.2.5.2, 0.2.6, 0.2.7, 0.2.7, 0.2.8
Change log	Changes.md
Dependencies	base (>=1.0 && <5), containers (>=0.1 && <0.7), random (>=1.0 && <1.3), special-functors (>=1.0 && <1.1), transformers (>=0.0.1 && <0.6), utility-ht (>=0.0.6 && <0.1) [details]
License	BSD-3-Clause
Author	Martin Erwig <erwig@eecs.oregonstate.edu>, Steve Kollmansberger
Maintainer	Henning Thielemann <haskell@henning-thielemann.de>
Category	Math, Monads, Graphics
Home page	http://www.haskell.org/haskellwiki/Probabilistic_Functional_Programming
Source repo	head: darcs get http://code.haskell.org/~thielema/probability/ this: darcs get http://code.haskell.org/~thielema/probability/ --tag 0.2.7
Uploaded	by HenningThielemann at 2020年07月28日T06:45:57Z

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probability-0.2.7.tar.gz [browse] (Cabal source package)
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Readme for probability-0.2.7

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Probabilistic Functional Programming in Haskell
Contact:
Martin Erwig, Oregon State University, erwig@eecs.oregonstate.edu
These files have been tested with GHC 6.4
Core Library files:
Show.hs		Pretty Printing
ListUtils.hs	
PrintList.hs	
Probability.hs	Core probabilistic module
Visualize.hs	Visualization system for use with R
Examples:
Barber.hs		An example of the queueing system
BayesianNetwork.hs	Implementing Bayesian networks
Boys.hs			A statistical examples
NBoys.hs		A generalized version of the previous
Collection.hs		Collections and two examples:
			Marbles and cards
Dice.hs			Rolling dice
MontyHall.hs		The "Monty Hall" Game (statistical)
Predator.hs		Non-probabilistic, demonstrates visualization
TreeGrowth.hs		A simple tree growth example
Visualize output is placed in the file FuSE.R which can be loaded into the 
R statistical program to see visualizations.
Randomized values can be displayed to the console using the printR 
function, which shows the value from a IO monad function.