Class GammaProductOp_Laplace

Provides outgoing messages for Product(Double, Double), given random arguments to the function.

Inheritance

Object

GammaProductOp_Laplace

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(Factor), "Product", new Type[]{typeof(double), typeof(double)})]
[Buffers(new string[]{"Q"})]
[Quality(QualityBand.Experimental)]
public static class GammaProductOp_Laplace

Fields

ForceProper

Declaration

public static bool ForceProper

Field Value

Type	Description
Boolean

Methods

AAverageConditional(Gamma, Gamma, Gamma, Gamma)

EP message to a.

Declaration

public static Gamma AAverageConditional(Gamma product, Gamma A, Gamma B, Gamma q)

Parameters

Type	Name	Description
Gamma	product	Incoming message from product. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	A	Incoming message from a.
Gamma	B	Incoming message from b. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	q	Buffer q.

Returns

Type	Description
Gamma	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.

BAverageConditional(Gamma, Gamma, Gamma, Gamma)

EP message to b.

Declaration

public static Gamma BAverageConditional(Gamma product, Gamma A, Gamma B, Gamma q)

Parameters

Type	Name	Description
Gamma	product	Incoming message from product. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	A	Incoming message from a. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	B	Incoming message from b. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	q	Buffer q.

Returns

Type	Description
Gamma	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.
ImproperMessageException	B is not a proper distribution.

LogAverageFactor(Gamma, Gamma, Gamma, Gamma)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Gamma product, Gamma A, Gamma B, Gamma q)

Parameters

Type	Name	Description
Gamma	product	Incoming message from product.
Gamma	A	Incoming message from a.
Gamma	B	Incoming message from b.
Gamma	q	Buffer q.

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)).

LogEvidenceRatio(Gamma, Gamma, Gamma, Gamma, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Gamma product, Gamma A, Gamma B, Gamma to_product, Gamma q)

Parameters

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

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.

LogEvidenceRatio(Double, Gamma, Gamma, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double product, Gamma A, Gamma B, Gamma q)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Gamma	A	Incoming message from a.
Gamma	B	Incoming message from b.
Gamma	q	Buffer q.

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(product,a,b)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

ProductAverageConditional(Gamma, Gamma, Gamma, Gamma)

EP message to product.

Declaration

public static Gamma ProductAverageConditional(Gamma product, Gamma A, Gamma B, Gamma q)

Parameters

Type	Name	Description
Gamma	product	Incoming message from product.
Gamma	A	Incoming message from a. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	B	Incoming message from b. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	q	Buffer q.

Returns

Type	Description
Gamma	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.

Q(Gamma, Gamma, Gamma)

Update the buffer Q.

Declaration

public static Gamma Q(Gamma product, Gamma A, Gamma B)

Parameters

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

Returns

Type	Description
Gamma	New value of buffer Q.

Remarks

Exceptions

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

QInit()

Initialize the buffer Q.

Declaration

public static Gamma QInit()

Returns

Type	Description
Gamma	Initial value of buffer Q.

Remarks

ToGamma(GammaPower)

Declaration

public static Gamma ToGamma(GammaPower gammaPower)

Parameters

Type	Name	Description
GammaPower	gammaPower

Returns

Type	Description
Gamma