# 18.1 Random variables and probability spaces

Module: sage.probability.random_variable

Random variables and probability spaces

This introduces a class of random variables, with the focus on discrete random variables (i.e. on a discrete probability space). This avoids the problem of defining a measure space and measurable functions.

Module-level Functions

 is_DiscreteProbabilitySpace( S)

 is_DiscreteRandomVariable( X)

 is_ProbabilitySpace( S)

 is_RandomVariable( X)

Class: DiscreteProbabilitySpace

class DiscreteProbabilitySpace
The discrete probability space
 DiscreteProbabilitySpace( self, X, P, [codomain=None], [check=False])
Create the discrete probability space with probabilities on the space X given by the dictionary P with values in the field real_field.

sage: S = [ i for i in range(16) ]
sage: P = {}
sage: for i in range(15): P[i] = 2^(-i-1)
sage: P[15] = 2^-16
sage: X = DiscreteProbabilitySpace(S,P)
sage: X.domain()
(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
sage: X.set()
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}
sage: X.entropy()
1.9997253418


A probability space can be defined on any list of elements.

sage: AZ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
sage: S = [ AZ[i] for i in range(26) ]
sage: P = { 'A':1/2, 'B':1/4, 'C':1/4 }
sage: X = DiscreteProbabilitySpace(S,P)
sage: X
Discrete probability space defined by {'A': 1/2, 'C': 1/4, 'B': 1/4}
sage: X.entropy()
1.5


Functions: entropy, set

 entropy( self)
The entropy of the probability space.

 set( self)
The set of values of the probability space taking possibly nonzero probability (a subset of the domain).

Special Functions: __repr__

Class: DiscreteRandomVariable

class DiscreteRandomVariable
A random variable on a discrete probability space.
 DiscreteRandomVariable( self, X, f, [codomain=None], [check=False])
Create free binary string monoid on generators.

INPUT:
x: A probability space
f: A dictionary such that X[x] = value for  x in X
is the discrete function on X


Functions: correlation, covariance, expectation, function, standard_deviation, translation_correlation, translation_covariance, translation_expectation, translation_standard_deviation, translation_variance, variance

 correlation( self, other)
The correlation of the probability space X = self with Y = other.

 covariance( self, other)
The covariance of the discrete random variable X = self with Y = other.

Let be the probability space of = self, with probability function , and be the expectation of . Then the variance of is:

cov

 expectation( self)
The expectation of the discrete random variable, namely , where = self and is the probability space of .

 function( self)
The function defining the random variable.

 standard_deviation( self)
The standard deviation of the discrete random variable.

Let be the probability space of = self, with probability function , and be the expectation of . Then the standard deviation of is defined to be

 translation_correlation( self, other, map)
The correlation of the probability space X = self with image of Y = other under map.

 translation_covariance( self, other, map)
The covariance of the probability space X = self with image of Y = other under the given map of the probability space.

Let be the probability space of = self, with probability function , and be the expectation of . Then the variance of is:

cov

 translation_expectation( self, map)
The expectation of the discrete random variable, namely , where = self, is the probability space of , and = map.

 translation_standard_deviation( self, map)
The standard deviation of the translated discrete random variable , where = self and = map.

Let be the probability space of = self, with probability function , and be the expectation of . Then the standard deviation of is defined to be

 translation_variance( self, map)
The variance of the discrete random variable , where = self, and = map.

Let be the probability space of = self, with probability function , and be the expectation of . Then the variance of is:

 variance( self)
The variance of the discrete random variable.

Let be the probability space of = self, with probability function , and be the expectation of . Then the variance of is:

Special Functions: __call__, __repr__

 __call__( self, x)
Return the value of the random variable at x.

Class: ProbabilitySpace_generic

class ProbabilitySpace_generic
A probability space.
 ProbabilitySpace_generic( self, domain, RR)
A generic probability space on given domain space and codomain ring.

Functions: domain

Class: RandomVariable_generic

class RandomVariable_generic
A random variable.
 RandomVariable_generic( self, X, RR)

Functions: codomain, domain, field, probability_space