Class ProductGaussianGammaVmpOp

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

Inheritance

Object

ProductGaussianGammaVmpOp

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 ProductGaussianGammaVmpOp

Methods

AAverageLogarithm(Gaussian, Gamma)

VMP message to a.

Declaration

public static Gaussian AAverageLogarithm(Gaussian Product, Gamma B)

Parameters

Type	Name	Description
Gaussian	Product	Incoming message from product. 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
Gaussian	The outgoing VMP message to the a argument.

Remarks

The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except a. Because the factor is deterministic, product is integrated out before taking the logarithm. The formula is exp(sum_(b) p(b) log(sum_product p(product) factor(product,a,b))).

Exceptions

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

AAverageLogarithm(Double, Gamma)

VMP message to a.

Declaration

[NotSupported("Variational Message Passing does not support a Product factor with fixed output and two random inputs.")]
public static Gaussian AAverageLogarithm(double Product, Gamma B)

Parameters

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

Returns

Type	Description
Gaussian	The outgoing VMP message to the a argument.

Remarks

The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except a. The formula is exp(sum_(b) p(b) log(factor(product,a,b))).

Exceptions

Type	Condition
ImproperMessageException	B is not a proper distribution.

BAverageLogarithm(Gaussian, Gaussian, Gamma, Gamma)

VMP message to b.

Declaration

public static Gamma BAverageLogarithm(Gaussian Product, Gaussian A, Gamma B, Gamma 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. 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	to_B	Previous outgoing message to B.

Returns

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

Remarks

The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except b. Because the factor is deterministic, product is integrated out before taking the logarithm. The formula is exp(sum_(a) p(a) log(sum_product p(product) 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.

BAverageLogarithm(Gaussian, Double, Gamma, Gamma)

VMP message to b.

Declaration

public static Gamma BAverageLogarithm(Gaussian Product, double A, Gamma B, Gamma to_B)

Parameters

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

Returns

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

Remarks

The outgoing message is the factor viewed as a function of b with product integrated out. The formula is sum_product p(product) factor(product,a,b).

Exceptions

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

BAverageLogarithm(Double, Gaussian)

VMP message to b.

Declaration

[NotSupported("Variational Message Passing does not support a Product factor with fixed output and two random inputs.")]
public static Gamma BAverageLogarithm(double Product, Gaussian A)

Parameters

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

Returns

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

Remarks

The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except b. The formula is exp(sum_(a) p(a) log(factor(product,a,b))).

BAverageLogarithm(Double, Double)

VMP message to b.

Declaration

public static Gamma BAverageLogarithm(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 VMP message to the b argument.

Remarks

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

ProductAverageLogarithm(Gaussian, Gamma)

VMP message to product.

Declaration

public static Gaussian ProductAverageLogarithm(Gaussian A, Gamma B)

Parameters

Type	Name	Description
Gaussian	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
Gaussian	The outgoing VMP 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[sum_(a,b) p(a,b) factor(product,a,b)].

Exceptions

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