Class BetaFromMeanAndVarianceOp

Provides outgoing messages for SampleFromMeanAndVariance(Double, Double), given random arguments to the function.

Inheritance

Object

BetaFromMeanAndVarianceOp

Inherited Members

Object.Equals(Object)

Object.Equals(Object, Object)

Object.GetHashCode()

Object.GetType()

Object.MemberwiseClone()

Object.ReferenceEquals(Object, Object)

Object.ToString()

Namespace: Microsoft.ML.Probabilistic.Factors

Assembly: Microsoft.ML.Probabilistic.dll

Syntax

[FactorMethod(new string[]{"sample", "mean", "variance"}, typeof(Beta), "SampleFromMeanAndVariance", new Type[]{})]
[Quality(QualityBand.Stable)]
public static class BetaFromMeanAndVarianceOp

Methods

AverageLogFactor(Beta, Double, Double, Beta)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(Beta sample, double mean, double variance, Beta to_sample)

Parameters

Type	Name	Description
Beta	sample	Incoming message from sample.
Double	mean	Constant value for mean.
Double	variance	Constant value for variance.
Beta	to_sample	Outgoing message to sample.

Returns

Type	Description
Double	Average of the factor's log-value across the given argument distributions.

Remarks

The formula for the result is sum_(sample) p(sample) log(factor(sample,mean,variance)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

AverageLogFactor(Double, Double, Double)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(double sample, double mean, double variance)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	mean	Constant value for mean.
Double	variance	Constant value for variance.

Returns

Type	Description
Double	Average of the factor's log-value across the given argument distributions.

Remarks

The formula for the result is log(factor(sample,mean,variance)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

LogAverageFactor(Beta, Double, Double, Beta)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Beta sample, double mean, double variance, Beta to_sample)

Parameters

Type	Name	Description
Beta	sample	Incoming message from sample.
Double	mean	Constant value for mean.
Double	variance	Constant value for variance.
Beta	to_sample	Outgoing message to sample.

Returns

Type	Description
Double	Logarithm of the factor's average value across the given argument distributions.

Remarks

The formula for the result is log(sum_(sample) p(sample) factor(sample,mean,variance)).

LogAverageFactor(Double, Double, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double sample, double mean, double variance)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	mean	Constant value for mean.
Double	variance	Constant value for variance.

Returns

Type	Description
Double	Logarithm of the factor's average value across the given argument distributions.

Remarks

The formula for the result is log(factor(sample,mean,variance)).

LogEvidenceRatio(Beta, Double, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Beta sample, double mean, double variance)

Parameters

Type	Name	Description
Beta	sample	Incoming message from sample.
Double	mean	Constant value for mean.
Double	variance	Constant value for variance.

Returns

Type	Description
Double	Logarithm of the factor's contribution the EP model evidence.

Remarks

The formula for the result is log(sum_(sample) p(sample) factor(sample,mean,variance) / sum_sample p(sample) messageTo(sample)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Double, Double, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double sample, double mean, double variance)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	mean	Constant value for mean.
Double	variance	Constant value for variance.

Returns

Type	Description
Double	Logarithm of the factor's contribution the EP model evidence.

Remarks

The formula for the result is log(factor(sample,mean,variance)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

SampleAverageConditional(Double, Double)

EP message to sample.

Declaration

public static Beta SampleAverageConditional(double mean, double variance)

Parameters

Type	Name	Description
Double	mean	Constant value for mean.
Double	variance	Constant value for variance.

Returns

Type	Description
Beta	The outgoing EP message to the sample argument.

Remarks

The outgoing message is the factor viewed as a function of sample conditioned on the given values.

SampleAverageLogarithm(Double, Double)

VMP message to sample.

Declaration

public static Beta SampleAverageLogarithm(double mean, double variance)

Parameters

Type	Name	Description
Double	mean	Constant value for mean.
Double	variance	Constant value for variance.

Returns

Type	Description
Beta	The outgoing VMP message to the sample argument.

Remarks

The outgoing message is the factor viewed as a function of sample conditioned on the given values.