[FactorMethod(typeof(Factor), "Product", new Type[]{typeof(double), typeof(double)})]
[Quality(QualityBand.Experimental)]
public class GaussianProductOp_Laplace2 : GaussianProductOpEvidenceBase

Remarks

This class allows EP to process the product factor using Laplace's method.

Fields

modified

Declaration

public static bool modified

Field Value

Type	Description
Boolean

offDiagonal

Declaration

public static bool offDiagonal

Field Value

Type	Description
Boolean

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`. 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_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).

Exceptions

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

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`. 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_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).

Exceptions

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

LogAverageFactor(Gaussian, Gaussian, Gaussian, Gaussian)

Evidence message for EP.

Declaration

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)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(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`. Must be a proper distribution. If uniform, the result will be uniform.

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, Gaussian, Gaussian, Gaussian)

EP message to product.

Declaration

public static Gaussian ProductAverageConditional(Gaussian Product, Gaussian A, Gaussian B, Gaussian to_A, 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_A	Previous outgoing message to `A`.
Gaussian	to_B	Previous outgoing message to `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).