Class VectorGaussianFromMeanAndVarianceOp

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

Inheritance

Object

VectorGaussianFromMeanAndVarianceOp

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(VectorGaussian), "SampleFromMeanAndVariance", new Type[]{})]
[Quality(QualityBand.Stable)]
public static class VectorGaussianFromMeanAndVarianceOp

Methods

AverageLogFactor(VectorGaussian, VectorGaussian)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(VectorGaussian sample, VectorGaussian to_sample)

Parameters

Type	Name	Description
VectorGaussian	sample	Incoming message from sample.
VectorGaussian	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(Vector, VectorGaussian, VectorGaussian)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(Vector sample, VectorGaussian mean, VectorGaussian to_mean)

Parameters

Type	Name	Description
Vector	sample	Constant value for sample.
VectorGaussian	mean	Incoming message from mean.
VectorGaussian	to_mean	Outgoing message to mean.

Returns

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

Remarks

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

AverageLogFactor(Vector, Vector, PositiveDefiniteMatrix)

Evidence message for VMP.

Declaration

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

Parameters

Type	Name	Description
Vector	sample	Constant value for sample.
Vector	mean	Constant value for mean.
PositiveDefiniteMatrix	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(VectorGaussian, VectorGaussian)

Evidence message for EP.

Declaration

public static double LogAverageFactor(VectorGaussian sample, VectorGaussian to_sample)

Parameters

Type	Name	Description
VectorGaussian	sample	Incoming message from sample.
VectorGaussian	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(Vector, Vector, PositiveDefiniteMatrix)

Evidence message for EP.

Declaration

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

Parameters

Type	Name	Description
Vector	sample	Constant value for sample.
Vector	mean	Constant value for mean.
PositiveDefiniteMatrix	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(VectorGaussian, VectorGaussian, PositiveDefiniteMatrix)

Declaration

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

Parameters

Type	Name	Description
VectorGaussian	sample
VectorGaussian	mean
PositiveDefiniteMatrix	variance

Returns

Type	Description
Double

LogEvidenceRatio(VectorGaussian, Vector, PositiveDefiniteMatrix)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(VectorGaussian sample, Vector mean, PositiveDefiniteMatrix variance)

Parameters

Type	Name	Description
VectorGaussian	sample	Incoming message from sample.
Vector	mean	Constant value for mean.
PositiveDefiniteMatrix	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(Vector, VectorGaussian, PositiveDefiniteMatrix)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Vector sample, VectorGaussian mean, PositiveDefiniteMatrix variance)

Parameters

Type	Name	Description
Vector	sample	Constant value for sample.
VectorGaussian	mean	Incoming message from mean.
PositiveDefiniteMatrix	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_(mean) p(mean) factor(sample,mean,variance)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Vector, Vector, PositiveDefiniteMatrix)

Evidence message for EP.

Declaration

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

Parameters

Type	Name	Description
Vector	sample	Constant value for sample.
Vector	mean	Constant value for mean.
PositiveDefiniteMatrix	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.

MeanAverageConditional(VectorGaussian, PositiveDefiniteMatrix, VectorGaussian)

Declaration

public static VectorGaussian MeanAverageConditional(VectorGaussian sample, PositiveDefiniteMatrix variance, VectorGaussian result)

Parameters

Type	Name	Description
VectorGaussian	sample
PositiveDefiniteMatrix	variance
VectorGaussian	result

Returns

Type	Description
VectorGaussian

MeanAverageConditional(Vector, PositiveDefiniteMatrix)

EP message to mean.

Declaration

public static VectorGaussian MeanAverageConditional(Vector sample, PositiveDefiniteMatrix variance)

Parameters

Type	Name	Description
Vector	sample	Constant value for sample.
PositiveDefiniteMatrix	variance	Constant value for variance.

Returns

Type	Description
VectorGaussian	The outgoing EP message to the mean argument.

Remarks

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

MeanAverageLogarithm(Vector, PositiveDefiniteMatrix)

VMP message to mean.

Declaration

public static VectorGaussian MeanAverageLogarithm(Vector sample, PositiveDefiniteMatrix variance)

Parameters

Type	Name	Description
Vector	sample	Constant value for sample.
PositiveDefiniteMatrix	variance	Constant value for variance.

Returns

Type	Description
VectorGaussian	The outgoing VMP message to the mean argument.

Remarks

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

SampleAverageConditional(VectorGaussian, PositiveDefiniteMatrix, VectorGaussian)

Declaration

public static VectorGaussian SampleAverageConditional(VectorGaussian mean, PositiveDefiniteMatrix variance, VectorGaussian result)

Parameters

Type	Name	Description
VectorGaussian	mean
PositiveDefiniteMatrix	variance
VectorGaussian	result

Returns

Type	Description
VectorGaussian

SampleAverageConditional(Vector, PositiveDefiniteMatrix)

EP message to sample.

Declaration

public static VectorGaussian SampleAverageConditional(Vector mean, PositiveDefiniteMatrix variance)

Parameters

Type	Name	Description
Vector	mean	Constant value for mean.
PositiveDefiniteMatrix	variance	Constant value for variance.

Returns

Type	Description
VectorGaussian	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(Vector, PositiveDefiniteMatrix)

VMP message to sample.

Declaration

public static VectorGaussian SampleAverageLogarithm(Vector mean, PositiveDefiniteMatrix variance)

Parameters

Type	Name	Description
Vector	mean	Constant value for mean.
PositiveDefiniteMatrix	variance	Constant value for variance.

Returns

Type	Description
VectorGaussian	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.