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Class RandomConditional<X, Y>
X: The type whose probability depends on Y.
Y: The type that affects the probability of X.

System.Object
Cognitoware.Mathematics.Probability.RandomConditional<X, Y>

Summary

The abstract base class for a conditional probability distribution.

Method Summary

BayesianInference(X, RandomDistribution<Y>)
Applies Bayes' Rule to the conditional distribution using a specific value of X.
BayesianInference(X, RandomDistribution<Y>)
Applies Bayes' Rule to the conditional distribution using a specific value of X and attempts to convert the mean to a specific implementation of RandomDistribution.
ConditionalProbabilityOf(X, Y)
Calculates the probability of a specific value result given a specific value range.
ConditionBy(Y)
Creates a probability distribution over X conditioned on a specific value of Y.
Equals(Object)
Inherited from System.Object
Finalize()
Inherited from System.Object
GetHashCode()
Inherited from System.Object
GetType()
Inherited from System.Object
LikelihoodOf(X)
Creates a likelihood function for a specific value of result.
Marginalize(RandomDistribution<Y>)
Creates a probability distribution of X given a random distribution across Y.
MemberwiseClone()
Inherited from System.Object
ToString()
Inherited from System.Object

Details

Calculates the probability of X given Y. This is represented by the equation P(X|Y). This class does not provide a representation of the condition distribution. Classes like ConditionalMap and LikelihoodMap provide specific data structures that are useful in different circumstances.

Constructor Details

family RandomConditional()

Method Details

public virtual RandomDistribution<Y> BayesianInference(X data, RandomDistribution<Y> prior)
Applies Bayes' Rule to the conditional distribution using a specific value of X.

Parameters:

`prior` - The prior belief of Y.
`data` - The observed value of X.

Returns:

The probability distribution of Y conditioned on the prior value and the observed data.

public virtual RandomDistribution<Y> BayesianInference(X data, RandomDistribution<Y> prior)
Applies Bayes' Rule to the conditional distribution using a specific value of X and attempts to convert the mean to a specific implementation of RandomDistribution.

Parameters:

`prior` - The prior belief of Y.
`data` - The observed value of X.

Returns:

The probability distribution of Y conditioned on the prior value and the observed data.

public virtual Double ConditionalProbabilityOf(X x, Y y)
Calculates the probability of a specific value result given a specific value range.

Parameters:

`x` - The value whose probability is being calculated.
`y` - The value that affects the probability of result.

Returns:

The conditional probability of result given range.

public virtual RandomDistribution<X> ConditionBy(Y y)
Creates a probability distribution over X given a known value y.

Parameters:

`y` - The value of Y over which the conditional distribution.

Returns:

The probability of X given the specific value of Y.

public virtual RandomDistribution<Y> LikelihoodOf(X x)
Creates a likelihood function for a specific value of result.

Parameters:

`x` - A conditional value in X.

Returns:

The likelihood of observing values in Y give the value of result.

public virtual RandomDistribution<X> Marginalize(RandomDistribution<Y> y)
Creates a probability distribution of X given a random distribution across Y. The probability of a value of X in this distribution is equal to the average conditional probability of X given Y (defined by this distribution) weighted by the probability of each corresponding value Y in the parameter distribution.

Parameters:

`y` - The distribuiton across Y that is used to weight the probability distributions of X.

Returns:

The expectation of X given the probable values of Y.