Class PlusGammaVmpOp

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

Inheritance

Object

PlusGammaVmpOp

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), "Plus", new Type[]{typeof(double), typeof(double)}, Default = true)]
[Quality(QualityBand.Experimental)]
public static class PlusGammaVmpOp

Methods

AAverageLogarithm(Gamma, Gamma, Gamma)

VMP message to A.

Declaration

public static Gamma AAverageLogarithm(Gamma sum, Gamma a, Gamma b)

Parameters

Type	Name	Description
Gamma	sum	Incoming message from Sum. 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.

Returns

Type	Description
Gamma	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, Sum is integrated out before taking the logarithm. The formula is exp(sum_(B) p(B) log(sum_Sum p(Sum) factor(Sum,A,B))).

Exceptions

Type	Condition
ImproperMessageException	sum is not a proper distribution.
ImproperMessageException	a is not a proper distribution.
ImproperMessageException	b is not a proper distribution.

AAverageLogarithm(GammaPower, GammaPower, GammaPower, GammaPower, GammaPower)

Declaration

public static GammaPower AAverageLogarithm(GammaPower sum, GammaPower a, GammaPower b, GammaPower to_a, GammaPower to_b)

Parameters

Type	Name	Description
GammaPower	sum
GammaPower	a
GammaPower	b
GammaPower	to_a
GammaPower	to_b

Returns

Type	Description
GammaPower

BAverageLogarithm(Gamma, Gamma, Gamma)

VMP message to B.

Declaration

public static Gamma BAverageLogarithm(Gamma sum, Gamma a, Gamma b)

Parameters

Type	Name	Description
Gamma	sum	Incoming message from Sum. 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.

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, Sum is integrated out before taking the logarithm. The formula is exp(sum_(A) p(A) log(sum_Sum p(Sum) factor(Sum,A,B))).

Exceptions

Type	Condition
ImproperMessageException	sum is not a proper distribution.
ImproperMessageException	a is not a proper distribution.
ImproperMessageException	b is not a proper distribution.

BAverageLogarithm(GammaPower, GammaPower, GammaPower, GammaPower, GammaPower)

Declaration

public static GammaPower BAverageLogarithm(GammaPower sum, GammaPower a, GammaPower b, GammaPower to_a, GammaPower to_b)

Parameters

Type	Name	Description
GammaPower	sum
GammaPower	a
GammaPower	b
GammaPower	to_a
GammaPower	to_b

Returns

Type	Description
GammaPower

InvGammaFromMeanAndMeanInverse(Double, Double)

Declaration

public static GammaPower InvGammaFromMeanAndMeanInverse(double mean, double meanInverse)

Parameters

Type	Name	Description
Double	mean
Double	meanInverse

Returns

Type	Description
GammaPower

InvGammaFromShapeAndMeanInverse(Double, Double)

Declaration

public static GammaPower InvGammaFromShapeAndMeanInverse(double shape, double meanInverse)

Parameters

Type	Name	Description
Double	shape
Double	meanInverse

Returns

Type	Description
GammaPower

MeanInverseOfSum(GammaPower, GammaPower)

Declaration

public static double MeanInverseOfSum(GammaPower a, GammaPower b)

Parameters

Type	Name	Description
GammaPower	a
GammaPower	b

Returns

Type	Description
Double

SumAverageLogarithm(Gamma, Gamma)

VMP message to Sum.

Declaration

public static Gamma SumAverageLogarithm(Gamma a, Gamma b)

Parameters

Type	Name	Description
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	The outgoing VMP message to the Sum argument.

Remarks

The outgoing message is a distribution matching the moments of Sum as the random arguments are varied. The formula is proj[sum_(A,B) p(A,B) factor(Sum,A,B)].

Exceptions

Type	Condition
ImproperMessageException	a is not a proper distribution.
ImproperMessageException	b is not a proper distribution.

SumAverageLogarithm(GammaPower, GammaPower, GammaPower)

VMP message to Sum.

Declaration

public static GammaPower SumAverageLogarithm(GammaPower a, GammaPower b, GammaPower result)

Parameters

Type	Name	Description
GammaPower	a	Incoming message from A. Must be a proper distribution. If uniform, the result will be uniform.
GammaPower	b	Incoming message from B. Must be a proper distribution. If uniform, the result will be uniform.
GammaPower	result	Modified to contain the outgoing message.

Returns

Type	Description
GammaPower	result

Remarks

The outgoing message is a distribution matching the moments of Sum as the random arguments are varied. The formula is proj[sum_(A,B) p(A,B) factor(Sum,A,B)].

Exceptions

Type	Condition
ImproperMessageException	a is not a proper distribution.
ImproperMessageException	b is not a proper distribution.