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

Remarks

This class allows EP to process the product factor using Laplace Propagation with other variables marginalized out.

Methods

AAverageConditional(Gaussian, Gaussian, Gaussian, Gaussian)

EP message to a.

Declaration

public static Gaussian AAverageConditional(Gaussian Product, Gaussian A, Gaussian B, Gaussian to_A)

Parameters

Type	Name	Description
Gaussian	Product	Incoming message from `product`.
Gaussian	A	Incoming message from `a`.
Gaussian	B	Incoming message from `b`.
Gaussian	to_A	Previous outgoing message to `A`.

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).

BAverageConditional(Gaussian, Gaussian, Gaussian, Gaussian)

EP message to b.

Declaration

public static Gaussian BAverageConditional(Gaussian Product, Gaussian A, Gaussian B, Gaussian to_B)

Parameters

Type	Name	Description
Gaussian	Product	Incoming message from `product`.
Gaussian	A	Incoming message from `a`.
Gaussian	B	Incoming message from `b`.
Gaussian	to_B	Previous outgoing message to `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).

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_A)

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`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	to_A	Previous outgoing message to `A`.

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

LogEvidenceRatio(Gaussian, 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_A, 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`. 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.
Gaussian	to_A	Previous outgoing message to `A`.
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.
ImproperMessageException	`A` is not a proper distribution.
ImproperMessageException	`B` is not a proper distribution.

ProductAverageConditional(Gaussian, Gaussian)

EP message to product.

Declaration

public static Gaussian ProductAverageConditional(Gaussian A, Gaussian B)

Parameters

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

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).

ProductAverageConditional2(Gaussian, Gaussian, Gaussian, Gaussian)

Declaration

public static Gaussian ProductAverageConditional2(Gaussian Product, Gaussian A, Gaussian B, Gaussian to_A)

Parameters

Type	Name	Description
Gaussian	Product
Gaussian	A
Gaussian	B
Gaussian	to_A

Returns

Type	Description
Gaussian