[FactorMethod(typeof(Vector), "InnerProduct", new Type[]{}, Default = true)]
[Buffers(new string[]{"AVariance", "BVariance", "AMean", "BMean"})]
[Quality(QualityBand.Mature)]
public class InnerProductOp : InnerProductOpBase

Methods

AAverageConditional(Gaussian, VectorGaussian, VectorGaussian, VectorGaussian)

EP message to a.

Declaration

[NotSupported("An InnerProduct factor between two VectorGaussian variables is not yet implemented for Expectation Propagation.  Try using Variational Message Passing.")]
public static VectorGaussian AAverageConditional(Gaussian innerProduct, VectorGaussian A, VectorGaussian B, VectorGaussian result)

Parameters

Type	Name	Description
Gaussian	innerProduct	Incoming message from `innerProduct`.
VectorGaussian	A	Incoming message from `a`.
VectorGaussian	B	Incoming message from `b`. Must be a proper distribution. If any element is uniform, the result will be uniform.
VectorGaussian	result	Modified to contain the outgoing message.

Returns

Type	Description
VectorGaussian	`result`

Remarks

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

Exceptions

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

BAverageConditional(Gaussian, VectorGaussian, VectorGaussian, VectorGaussian)

EP message to b.

Declaration

[NotSupported("An InnerProduct factor between two VectorGaussian variables is not yet implemented for Expectation Propagation.  Try using Variational Message Passing.")]
public static VectorGaussian BAverageConditional(Gaussian innerProduct, VectorGaussian A, VectorGaussian B, VectorGaussian result)

Parameters

Type	Name	Description
Gaussian	innerProduct	Incoming message from `innerProduct`.
VectorGaussian	A	Incoming message from `a`. Must be a proper distribution. If any element is uniform, the result will be uniform.
VectorGaussian	B	Incoming message from `b`.
VectorGaussian	result	Modified to contain the outgoing message.

Returns

Type	Description
VectorGaussian	`result`

Remarks

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

Exceptions

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

InnerProductAverageConditional(VectorGaussian, VectorGaussian)

EP message to innerProduct.

Declaration

[NotSupported("An InnerProduct factor between two VectorGaussian variables is not yet implemented for Expectation Propagation.  Try using Variational Message Passing.")]
public static Gaussian InnerProductAverageConditional(VectorGaussian A, VectorGaussian B)

Parameters

Type	Name	Description
VectorGaussian	A	Incoming message from `a`. Must be a proper distribution. If any element is uniform, the result will be uniform.
VectorGaussian	B	Incoming message from `b`. Must be a proper distribution. If any element is uniform, the result will be uniform.

Returns

Type	Description
Gaussian	The outgoing EP message to the `innerProduct` argument.

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	`A` is not a proper distribution.
ImproperMessageException	`B` is not a proper distribution.

LogAverageFactor(Gaussian, VectorGaussian, VectorGaussian)

Evidence message for EP.

Declaration

[NotSupported("An InnerProduct factor between two VectorGaussian variables is not yet implemented for Expectation Propagation.  Try using Variational Message Passing.")]
public static double LogAverageFactor(Gaussian innerProduct, VectorGaussian A, VectorGaussian B)

Parameters

Type	Name	Description
Gaussian	innerProduct	Incoming message from `innerProduct`.
VectorGaussian	A	Incoming message from `a`.
VectorGaussian	B	Incoming message from `b`.

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_(innerProduct,a,b) p(innerProduct,a,b) factor(innerProduct,a,b)).

LogAverageFactor(Double, VectorGaussian, VectorGaussian)

Evidence message for EP.

Declaration

[NotSupported("An InnerProduct factor between two VectorGaussian variables is not yet implemented for Expectation Propagation.  Try using Variational Message Passing.")]
public static double LogAverageFactor(double innerProduct, VectorGaussian A, VectorGaussian B)

Parameters

Type	Name	Description
Double	innerProduct	Constant value for `innerProduct`.
VectorGaussian	A	Incoming message from `a`.
VectorGaussian	B	Incoming message from `b`.

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_(a,b) p(a,b) factor(innerProduct,a,b)).

LogEvidenceRatio(Gaussian, VectorGaussian, VectorGaussian)

Evidence message for EP.

Declaration

[NotSupported("An InnerProduct factor between two VectorGaussian variables is not yet implemented for Expectation Propagation.  Try using Variational Message Passing.")]
public static double LogEvidenceRatio(Gaussian innerProduct, VectorGaussian A, VectorGaussian B)

Parameters

Type	Name	Description
Gaussian	innerProduct	Incoming message from `innerProduct`.
VectorGaussian	A	Incoming message from `a`.
VectorGaussian	B	Incoming message from `b`.

Returns

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

Remarks

The formula for the result is log(sum_(innerProduct,a,b) p(innerProduct,a,b) factor(innerProduct,a,b) / sum_innerProduct p(innerProduct) messageTo(innerProduct)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Double, VectorGaussian, VectorGaussian)

Evidence message for EP.

Declaration

[NotSupported("An InnerProduct factor between two VectorGaussian variables is not yet implemented for Expectation Propagation.  Try using Variational Message Passing.")]
public static double LogEvidenceRatio(double innerProduct, VectorGaussian A, VectorGaussian B)

Parameters

Type	Name	Description
Double	innerProduct	Constant value for `innerProduct`.
VectorGaussian	A	Incoming message from `a`.
VectorGaussian	B	Incoming message from `b`.

Returns

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

Remarks

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