[FactorMethod(typeof(Factor), "Product", new Type[]{typeof(double), typeof(double)}, Default = true)]
[FactorMethod(new string[]{"A", "Product", "B"}, typeof(Factor), "Ratio", new Type[]{typeof(double), typeof(double)}, Default = true)]
[Quality(QualityBand.Mature)]
public class GaussianProductOp : GaussianProductOpEvidenceBase

Fields

ForceProper

Force proper messages

Declaration

public static bool ForceProper

Field Value

Type	Description
Boolean

QuadratureNodeCount

The number of quadrature nodes used to compute the messages. Reduce this number to save time in exchange for less accuracy. Must be an odd number.

Declaration

public static int QuadratureNodeCount

Field Value

Type	Description
Int32

Methods

AAverageConditional(Gaussian, Gaussian, Gaussian)

EP message to a.

Declaration

[Quality(QualityBand.Experimental)]
public static Gaussian AAverageConditional(Gaussian Product, Gaussian A, Gaussian B)

Parameters

Type	Name	Description
Gaussian	Product	Incoming message from `product`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	A	Incoming message from `a`.
Gaussian	B	Incoming message from `b`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

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

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_(product,b) p(product,b) factor(product,a,b)]/p(a).

Exceptions

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

AAverageConditional(Double, Gaussian, Gaussian)

EP message to a.

Declaration

[Quality(QualityBand.Experimental)]
public static Gaussian AAverageConditional(double Product, Gaussian A, Gaussian B)

Parameters

Type	Name	Description
Double	Product	Constant value for `product`.
Gaussian	A	Incoming message from `a`.
Gaussian	B	Incoming message from `b`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

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

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_(b) p(b) factor(product,a,b)]/p(a).

Exceptions

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

ADerivatives(Double, Double, Double, Gaussian, out Double, out Double)

Compute the derivatives of the logarithm of the marginalized factor.

Declaration

public static void ADerivatives(double a, double mB, double vB, Gaussian Product, out double dlogf, out double ddlogf)

Parameters

Type	Name	Description
Double	a	The point to evaluate the derivatives
Double	mB
Double	vB
Gaussian	Product
Double	dlogf
Double	ddlogf

BAverageConditional(Gaussian, Gaussian, Gaussian)

EP message to b.

Declaration

[Quality(QualityBand.Experimental)]
public static Gaussian BAverageConditional(Gaussian Product, Gaussian A, Gaussian B)

Parameters

Type	Name	Description
Gaussian	Product	Incoming message from `product`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	A	Incoming message from `a`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	B	Incoming message from `b`.

Returns

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

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_(product,a) p(product,a) factor(product,a,b)]/p(b).

Exceptions

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

BAverageConditional(Double, Gaussian, Gaussian)

EP message to b.

Declaration

[Quality(QualityBand.Experimental)]
public static Gaussian BAverageConditional(double Product, Gaussian A, Gaussian B)

Parameters

Type	Name	Description
Double	Product	Constant value for `product`.
Gaussian	A	Incoming message from `a`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	B	Incoming message from `b`.

Returns

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

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_(a) p(a) factor(product,a,b)]/p(b).

Exceptions

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

LogAverageFactor(Gaussian, Gaussian, Gaussian, Gaussian)

Evidence message for EP.

Declaration

[FactorMethod(typeof(Factor), "Product", new Type[]{typeof(double), typeof(double)})]
public static double LogAverageFactor(Gaussian Product, Gaussian A, Gaussian B, Gaussian to_product)

Parameters

Type	Name	Description
Gaussian	Product	Incoming message from `product`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	A	Incoming message from `a`.
Gaussian	B	Incoming message from `b`.
Gaussian	to_product	Previous outgoing message to `product`.

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

Exceptions

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

LogEvidenceRatio(Gaussian, Gaussian, Gaussian, Gaussian)

Evidence message for EP.

Declaration

[FactorMethod(typeof(Factor), "Product", new Type[]{typeof(double), typeof(double)})]
public static double LogEvidenceRatio(Gaussian Product, Gaussian A, Gaussian B, Gaussian to_product)

Parameters

Type	Name	Description
Gaussian	Product	Incoming message from `product`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	A	Incoming message from `a`.
Gaussian	B	Incoming message from `b`.
Gaussian	to_product	Previous outgoing message to `product`.

Returns

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

Remarks

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

Exceptions

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

ProductAverageConditional(Gaussian, Gaussian, Gaussian)

EP message to product.

Declaration

[Quality(QualityBand.Experimental)]
public static Gaussian ProductAverageConditional(Gaussian Product, Gaussian A, Gaussian B)

Parameters

Type	Name	Description
Gaussian	Product	Incoming message from `product`.
Gaussian	A	Incoming message from `a`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	B	Incoming message from `b`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

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

Remarks

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

Exceptions

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

ProductAverageConditionalInit(Gaussian, Gaussian)

Declaration

public static Gaussian ProductAverageConditionalInit(Gaussian A, Gaussian B)

Parameters

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

Returns

Type	Description
Gaussian