Class WishartFromShapeAndScaleOp

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

Inheritance

Object

WishartFromShapeAndScaleOp

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(typeof(Wishart), "SampleFromShapeAndScale", new Type[]{})]
[Quality(QualityBand.Stable)]
public static class WishartFromShapeAndScaleOp

Methods

AverageLogFactor(Wishart, Wishart)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(Wishart sample, Wishart to_sample)

Parameters

Type	Name	Description
Wishart	sample	Incoming message from sample.
Wishart	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,shape,scale)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

AverageLogFactor(PositiveDefiniteMatrix, Double, PositiveDefiniteMatrix)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(PositiveDefiniteMatrix sample, double shape, PositiveDefiniteMatrix scale)

Parameters

Type	Name	Description
PositiveDefiniteMatrix	sample	Constant value for sample.
Double	shape	Constant value for shape.
PositiveDefiniteMatrix	scale	Constant value for scale.

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,shape,scale)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

LogAverageFactor(Wishart, Wishart)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Wishart sample, Wishart to_sample)

Parameters

Type	Name	Description
Wishart	sample	Incoming message from sample.
Wishart	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,shape,scale)).

LogAverageFactor(PositiveDefiniteMatrix, Double, PositiveDefiniteMatrix)

Evidence message for EP.

Declaration

public static double LogAverageFactor(PositiveDefiniteMatrix sample, double shape, PositiveDefiniteMatrix scale)

Parameters

Type	Name	Description
PositiveDefiniteMatrix	sample	Constant value for sample.
Double	shape	Constant value for shape.
PositiveDefiniteMatrix	scale	Constant value for scale.

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,shape,scale)).

LogEvidenceRatio(Wishart, Double, PositiveDefiniteMatrix)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Wishart sample, double shape, PositiveDefiniteMatrix scale)

Parameters

Type	Name	Description
Wishart	sample	Incoming message from sample.
Double	shape	Constant value for shape.
PositiveDefiniteMatrix	scale	Constant value for scale.

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,shape,scale) / sum_sample p(sample) messageTo(sample)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(PositiveDefiniteMatrix, Double, PositiveDefiniteMatrix)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(PositiveDefiniteMatrix sample, double shape, PositiveDefiniteMatrix scale)

Parameters

Type	Name	Description
PositiveDefiniteMatrix	sample	Constant value for sample.
Double	shape	Constant value for shape.
PositiveDefiniteMatrix	scale	Constant value for scale.

Returns

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

Remarks

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

SampleAverageConditional(Double, PositiveDefiniteMatrix)

EP message to sample.

Declaration

public static Wishart SampleAverageConditional(double shape, PositiveDefiniteMatrix scale)

Parameters

Type	Name	Description
Double	shape	Constant value for shape.
PositiveDefiniteMatrix	scale	Constant value for scale.

Returns

Type	Description
Wishart	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, PositiveDefiniteMatrix)

VMP message to sample.

Declaration

public static Wishart SampleAverageLogarithm(double shape, PositiveDefiniteMatrix scale)

Parameters

Type	Name	Description
Double	shape	Constant value for shape.
PositiveDefiniteMatrix	scale	Constant value for scale.

Returns

Type	Description
Wishart	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.