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API for probabilities.monte-carlo - clojure-contrib v1.2 (beta 1)

by Konrad Hinsen
Usage: 
(ns your-namespace
  (:require clojure.contrib.probabilities.monte-carlo))

Overview


Monte-Carlo method support

Monte-Carlo methods transform an input random number stream
(usually having a continuous uniform distribution in the
interval [0, 1)) into a random number stream whose distribution
satisfies certain conditions (usually the expectation value
is equal to some desired quantity). They are thus
transformations from one probability distribution to another one.

This library represents a Monte-Carlo method by a function that
takes as input the state of a random number stream with
uniform distribution (see
clojure.contrib.probabilities.random-numbers) and returns a
vector containing one sample value of the desired output
distribution and the final state of the input random number
stream. Such functions are state monad values and can be
composed using operations defined in clojure.contrib.monads.






Public Variables and Functions



  

  

  
discrete

  function

  
Usage: (discrete dist)


  
A discrete distribution, defined by a map dist mapping values
to probabilities. The sum of probabilities must be one.

  
  
  Source


  

  

  
exponential

  function

  
Usage: (exponential lambda)


  
Transform a sequence of uniform random numbers in the interval [0, 1)
into a sequence of exponential random numbers with parameter lambda.

  
  
  Source


  

  

  
lognormal

  function

  
Usage: (lognormal mu sigma)


  
Transform a sequence of uniform random numbesr in the interval [0, 1)
into a sequence of lognormal random numbers with mean mu and standard
deviation sigma.

  
  
  Source


  

  

  
n-sphere

  function

  
Usage: (n-sphere n r)


  
Return a uniform distribution of n-dimensional vectors inside an
n-sphere of radius r.

  
  
  Source


  

  

  
normal

  function

  
Usage: (normal mu sigma)


  
Transform a sequence urs of uniform random number in the interval [0, 1)
into a sequence of normal random numbers with mean mu and standard
deviation sigma.

  
  
  Source


  

  

  
normal-box-muller

  function

  
Usage: (normal-box-muller mu sigma)


  
Transform a sequence of uniform random numbers in the interval [0, 1)
into a sequence of normal random numbers with mean mu and standard
deviation sigma.

  
  
  Source


  

  

  
random-stream

  function

  
Usage: (random-stream distribution random-stream-state)


  
Define a random stream by a distribution and the state of a
random number stream with uniform distribution in [0, 1).

  
  
  Source


  

  

  
reject

  function

  
Usage: (reject p dist)


  
Return the distribution that results from rejecting the values from
dist that do not satisfy predicate p.

  
  
  Source


  

  

  
sample

  function

  
Usage: (sample n dist)


  
Return the distribution of samples of length n from the
distribution dist

  
  
  Source


  

  

  
sample-mean

  function

  
Usage: (sample-mean n dist)


  
Return the distribution of the mean over n samples from the
distribution dist

  
  
  Source


  

  

  
sample-mean-variance

  function

  
Usage: (sample-mean-variance n dist)


  
Return the distribution of the mean-and-variance (a vector containing
the mean and the variance) over n samples from the distribution dist

  
  
  Source


  

  

  
sample-reduce

  function

  
Usage: (sample-reduce f n dist)
       (sample-reduce f val n dist)


  
Returns the distribution of the reduction of f over n samples from the
distribution dist.

  
  
  Source


  

  

  
sample-sum

  function

  
Usage: (sample-sum n dist)


  
Return the distribution of the sum over n samples from the
distribution dist.

  
  
  Source






              

            

          

	

	
      

      
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