Class VectorGaussianOp_Laplace2

Provides outgoing messages for the following factors:

, given random arguments to the function.

Inheritance

Object

VectorGaussianOp_Laplace2

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), "Sample", new Type[]{typeof(Vector), typeof(PositiveDefiniteMatrix)})]
[FactorMethod(new string[]{"sample", "mean", "precision"}, typeof(Factor), "VectorGaussian", new Type[]{})]
[Buffers(new string[]{"SampleMean", "SampleVariance", "MeanMean", "MeanVariance", "PrecisionMean", "PrecisionMeanLogDet"})]
[Quality(QualityBand.Preview)]
public static class VectorGaussianOp_Laplace2

Methods

LogAverageFactor(VectorGaussian, VectorGaussian, Wishart, Wishart)

Evidence message for EP.

Declaration

public static double LogAverageFactor(VectorGaussian sample, VectorGaussian mean, Wishart precision, Wishart to_precision)

Parameters

Type	Name	Description
VectorGaussian	sample	Incoming message from `sample`.
VectorGaussian	mean	Incoming message from `mean`.
Wishart	precision	Incoming message from `precision`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	to_precision	Previous outgoing message to `precision`.

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,mean,precision) p(sample,mean,precision) factor(sample,mean,precision)).

Exceptions

Type	Condition
ImproperMessageException	`precision` is not a proper distribution.

LogAverageFactor(Vector, VectorGaussian, Wishart, Wishart)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Vector sample, VectorGaussian mean, Wishart precision, Wishart to_precision)

Parameters

Type	Name	Description
Vector	sample	Constant value for `sample`.
VectorGaussian	mean	Incoming message from `mean`.
Wishart	precision	Incoming message from `precision`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	to_precision	Previous outgoing message to `precision`.

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_(mean,precision) p(mean,precision) factor(sample,mean,precision)).

Exceptions

Type	Condition
ImproperMessageException	`precision` is not a proper distribution.

LogEvidenceRatio(VectorGaussian, VectorGaussian, Wishart, Wishart, VectorGaussian)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(VectorGaussian sample, VectorGaussian mean, Wishart precision, Wishart to_precision, VectorGaussian to_sample)

Parameters

Type	Name	Description
VectorGaussian	sample	Incoming message from `sample`.
VectorGaussian	mean	Incoming message from `mean`.
Wishart	precision	Incoming message from `precision`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	to_precision	Previous outgoing message to `precision`.
VectorGaussian	to_sample	Previous outgoing message to `sample`.

Returns

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

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	`precision` is not a proper distribution.

LogEvidenceRatio(Vector, VectorGaussian, Wishart, Wishart)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Vector sample, VectorGaussian mean, Wishart precision, Wishart to_precision)

Parameters

Type	Name	Description
Vector	sample	Constant value for `sample`.
VectorGaussian	mean	Incoming message from `mean`.
Wishart	precision	Incoming message from `precision`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	to_precision	Previous outgoing message to `precision`.

Returns

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

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	`precision` is not a proper distribution.

MeanAverageConditional(VectorGaussian, Wishart, Wishart, VectorGaussian)

EP message to mean.

Declaration

public static VectorGaussian MeanAverageConditional(VectorGaussian sample, Wishart precision, Wishart to_precision, VectorGaussian result)

Parameters

Type	Name	Description
VectorGaussian	sample	Incoming message from `sample`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	precision	Incoming message from `precision`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	to_precision	Previous outgoing message to `precision`.
VectorGaussian	result	Modified to contain the outgoing message.

Returns

Type	Description
VectorGaussian	`result`

Remarks

The outgoing message is a distribution matching the moments of mean as the random arguments are varied. The formula is proj[p(mean) sum_(sample,precision) p(sample,precision) factor(sample,mean,precision)]/p(mean).

Exceptions

Type	Condition
ImproperMessageException	`sample` is not a proper distribution.
ImproperMessageException	`precision` is not a proper distribution.

PrecisionAverageConditional(VectorGaussian, VectorGaussian, Wishart, Wishart, Wishart)

EP message to precision.

Declaration

public static Wishart PrecisionAverageConditional(VectorGaussian sample, VectorGaussian mean, Wishart precision, Wishart to_precision, Wishart result)

Parameters

Type	Name	Description
VectorGaussian	sample	Incoming message from `sample`. Must be a proper distribution. If any element is uniform, the result will be uniform.
VectorGaussian	mean	Incoming message from `mean`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	precision	Incoming message from `precision`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	to_precision	Previous outgoing message to `precision`.
Wishart	result	Modified to contain the outgoing message.

Returns

Type	Description
Wishart	`result`

Remarks

The outgoing message is a distribution matching the moments of precision as the random arguments are varied. The formula is proj[p(precision) sum_(sample,mean) p(sample,mean) factor(sample,mean,precision)]/p(precision).

Exceptions

Type	Condition
ImproperMessageException	`sample` is not a proper distribution.
ImproperMessageException	`mean` is not a proper distribution.
ImproperMessageException	`precision` is not a proper distribution.

SampleAverageConditional(VectorGaussian, Wishart, Wishart, VectorGaussian)

EP message to sample.

Declaration

public static VectorGaussian SampleAverageConditional(VectorGaussian mean, Wishart precision, Wishart to_precision, VectorGaussian result)

Parameters

Type	Name	Description
VectorGaussian	mean	Incoming message from `mean`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	precision	Incoming message from `precision`. Must be a proper distribution. If any element is uniform, the result will be uniform.
Wishart	to_precision	Previous outgoing message to `precision`.
VectorGaussian	result	Modified to contain the outgoing message.

Returns

Type	Description
VectorGaussian	`result`

Remarks

The outgoing message is a distribution matching the moments of sample as the random arguments are varied. The formula is proj[p(sample) sum_(mean,precision) p(mean,precision) factor(sample,mean,precision)]/p(sample).

Exceptions

Type	Condition
ImproperMessageException	`mean` is not a proper distribution.
ImproperMessageException	`precision` is not a proper distribution.