Class GammaProductOp

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

Inheritance

Object

GammaProductOp

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)})]
[Quality(QualityBand.Preview)]
public static class GammaProductOp

Methods

AAverageConditional(Gamma, Double)

EP message to a.

Declaration

public static Gamma AAverageConditional(Gamma Product, double B)

Parameters

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

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

Exceptions

Type	Condition
ImproperMessageException	Product is not a proper distribution.

AAverageConditional(GammaPower, Double, GammaPower)

EP message to a.

Declaration

public static GammaPower AAverageConditional(GammaPower Product, double B, GammaPower result)

Parameters

Type	Name	Description
GammaPower	Product	Incoming message from product. Must be a proper distribution. If uniform, the result will be uniform.
Double	B	Constant value for b.
GammaPower	result	Modified to contain the outgoing message.

Returns

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

Exceptions

Type	Condition
ImproperMessageException	Product is not a proper distribution.

AAverageConditional(Double, Gamma)

EP message to a.

Declaration

public static GammaPower AAverageConditional(double product, Gamma B)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Gamma	B	Incoming message from b.

Returns

Type	Description
GammaPower	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).

AAverageConditional(Double, Double)

EP message to a.

Declaration

public static Gamma AAverageConditional(double Product, double B)

Parameters

Type	Name	Description
Double	Product	Constant value for product.
Double	B	Constant value for b.

Returns

Type	Description
Gamma	The outgoing EP message to the a argument.

Remarks

The outgoing message is the factor viewed as a function of a conditioned on the given values.

AAverageConditional(Double, Double, GammaPower)

EP message to a.

Declaration

public static GammaPower AAverageConditional(double Product, double B, GammaPower result)

Parameters

Type	Name	Description
Double	Product	Constant value for product.
Double	B	Constant value for b.
GammaPower	result	Modified to contain the outgoing message.

Returns

Type	Description
GammaPower	result

Remarks

The outgoing message is the factor viewed as a function of a conditioned on the given values.

BAverageConditional(Gamma, Double)

EP message to b.

Declaration

public static Gamma BAverageConditional(Gamma Product, double A)

Parameters

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

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

Exceptions

Type	Condition
ImproperMessageException	Product is not a proper distribution.

BAverageConditional(GammaPower, Double, GammaPower)

EP message to b.

Declaration

public static GammaPower BAverageConditional(GammaPower Product, double A, GammaPower result)

Parameters

Type	Name	Description
GammaPower	Product	Incoming message from product. Must be a proper distribution. If uniform, the result will be uniform.
Double	A	Constant value for a.
GammaPower	result	Modified to contain the outgoing message.

Returns

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

Exceptions

Type	Condition
ImproperMessageException	Product is not a proper distribution.

BAverageConditional(Double, Gamma)

EP message to b.

Declaration

public static GammaPower BAverageConditional(double product, Gamma A)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Gamma	A	Incoming message from a.

Returns

Type	Description
GammaPower	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).

BAverageConditional(Double, Double)

EP message to b.

Declaration

public static Gamma BAverageConditional(double Product, double A)

Parameters

Type	Name	Description
Double	Product	Constant value for product.
Double	A	Constant value for a.

Returns

Type	Description
Gamma	The outgoing EP message to the b argument.

Remarks

The outgoing message is the factor viewed as a function of b conditioned on the given values.

BAverageConditional(Double, Double, GammaPower)

EP message to b.

Declaration

public static GammaPower BAverageConditional(double Product, double A, GammaPower result)

Parameters

Type	Name	Description
Double	Product	Constant value for product.
Double	A	Constant value for a.
GammaPower	result	Modified to contain the outgoing message.

Returns

Type	Description
GammaPower	result

Remarks

The outgoing message is the factor viewed as a function of b conditioned on the given values.

LogAverageFactor(Gamma, Gamma, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Gamma product, Gamma a, double b)

Parameters

Type	Name	Description
Gamma	product	Incoming message from product.
Gamma	a	Incoming message from a.
Double	b	Constant value for 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_(product,a) p(product,a) factor(product,a,b)).

LogAverageFactor(Gamma, Double, Gamma)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Gamma product, double a, Gamma b)

Parameters

Type	Name	Description
Gamma	product	Incoming message from product.
Double	a	Constant value for a.
Gamma	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_(product,b) p(product,b) factor(product,a,b)).

LogAverageFactor(GammaPower, GammaPower, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(GammaPower product, GammaPower a, double b)

Parameters

Type	Name	Description
GammaPower	product	Incoming message from product.
GammaPower	a	Incoming message from a.
Double	b	Constant value for 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_(product,a) p(product,a) factor(product,a,b)).

LogAverageFactor(GammaPower, Double, GammaPower)

Evidence message for EP.

Declaration

public static double LogAverageFactor(GammaPower product, double a, GammaPower b)

Parameters

Type	Name	Description
GammaPower	product	Incoming message from product.
Double	a	Constant value for a.
GammaPower	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_(product,b) p(product,b) factor(product,a,b)).

LogAverageFactor(Double, Gamma, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double product, Gamma a, double b)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Gamma	a	Incoming message from a.
Double	b	Constant value for 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) p(a) factor(product,a,b)).

LogAverageFactor(Double, GammaPower, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double product, GammaPower a, double b)

Parameters

Type	Name	Description
Double	product	Constant value for product.
GammaPower	a	Incoming message from a.
Double	b	Constant value for 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) p(a) factor(product,a,b)).

LogAverageFactor(Double, Double, Gamma)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double product, double a, Gamma b)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Double	a	Constant value for a.
Gamma	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_(b) p(b) factor(product,a,b)).

LogAverageFactor(Double, Double, GammaPower)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double product, double a, GammaPower b)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Double	a	Constant value for a.
GammaPower	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_(b) p(b) factor(product,a,b)).

LogEvidenceRatio(Gamma, Gamma, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Gamma product, Gamma a, double b)

Parameters

Type	Name	Description
Gamma	product	Incoming message from product.
Gamma	a	Incoming message from a.
Double	b	Constant value for b.

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) p(product,a) 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.

LogEvidenceRatio(Gamma, Double, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Gamma product, double a, Gamma b)

Parameters

Type	Name	Description
Gamma	product	Incoming message from product.
Double	a	Constant value for a.
Gamma	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_(product,b) p(product,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.

LogEvidenceRatio(GammaPower, GammaPower, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(GammaPower product, GammaPower a, double b)

Parameters

Type	Name	Description
GammaPower	product	Incoming message from product.
GammaPower	a	Incoming message from a.
Double	b	Constant value for b.

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) p(product,a) 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.

LogEvidenceRatio(GammaPower, Double, GammaPower)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(GammaPower product, double a, GammaPower b)

Parameters

Type	Name	Description
GammaPower	product	Incoming message from product.
Double	a	Constant value for a.
GammaPower	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_(product,b) p(product,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.

LogEvidenceRatio(Double, Gamma, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double product, Gamma a, double b)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Gamma	a	Incoming message from a.
Double	b	Constant value for 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) p(a) factor(product,a,b)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Double, GammaPower, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double product, GammaPower a, double b)

Parameters

Type	Name	Description
Double	product	Constant value for product.
GammaPower	a	Incoming message from a.
Double	b	Constant value for 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) p(a) factor(product,a,b)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Double, Double, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double product, double a, Gamma b)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Double	a	Constant value for a.
Gamma	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_(b) p(b) factor(product,a,b)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Double, Double, GammaPower)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double product, double a, GammaPower b)

Parameters

Type	Name	Description
Double	product	Constant value for product.
Double	a	Constant value for a.
GammaPower	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_(b) p(b) factor(product,a,b)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

ProductAverageConditional(Gamma, Double)

EP message to product.

Declaration

public static Gamma ProductAverageConditional(Gamma A, double B)

Parameters

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

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

Exceptions

Type	Condition
ImproperMessageException	A is not a proper distribution.

ProductAverageConditional(GammaPower, Double)

EP message to product.

Declaration

public static GammaPower ProductAverageConditional(GammaPower A, double B)

Parameters

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

Returns

Type	Description
GammaPower	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) p(a) factor(product,a,b)]/p(product).

Exceptions

Type	Condition
ImproperMessageException	A is not a proper distribution.

ProductAverageConditional(Double, Gamma)

EP message to product.

Declaration

public static Gamma ProductAverageConditional(double A, Gamma B)

Parameters

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

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

Exceptions

Type	Condition
ImproperMessageException	B is not a proper distribution.

ProductAverageConditional(Double, GammaPower)

EP message to product.

Declaration

public static GammaPower ProductAverageConditional(double A, GammaPower B)

Parameters

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

Returns

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

Exceptions

Type	Condition
ImproperMessageException	B is not a proper distribution.

ProductAverageConditional(Double, Double)

EP message to product.

Declaration

public static Gamma ProductAverageConditional(double a, double b)

Parameters

Type	Name	Description
Double	a	Constant value for a.
Double	b	Constant value for b.

Returns

Type	Description
Gamma	The outgoing EP message to the product argument.

Remarks

The outgoing message is the factor viewed as a function of product conditioned on the given values.