Class ExpOp_LaplaceProp

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

Inheritance

Object

ExpOp_LaplaceProp

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(Math), "Exp", new Type[]{typeof(double)})]
[Quality(QualityBand.Experimental)]
public static class ExpOp_LaplaceProp

Fields

ForceProper

Declaration

public static bool ForceProper

Field Value

Type	Description
Boolean

Methods

DAverageConditional(Gamma, Gaussian, Gaussian)

EP message to d.

Declaration

public static Gaussian DAverageConditional(Gamma exp, Gaussian d, Gaussian to_d)

Parameters

Type	Name	Description
Gamma	exp	Incoming message from `exp`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	d	Incoming message from `d`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	to_d	Previous outgoing message to `d`.

Returns

Type	Description
Gaussian	The outgoing EP message to the `d` argument.

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	`exp` is not a proper distribution.
ImproperMessageException	`d` is not a proper distribution.

ExpAverageConditional(Gamma, Gaussian, Gaussian)

EP message to exp.

Declaration

public static Gamma ExpAverageConditional(Gamma exp, Gaussian d, Gaussian to_d)

Parameters

Type	Name	Description
Gamma	exp	Incoming message from `exp`.
Gaussian	d	Incoming message from `d`.
Gaussian	to_d	Previous outgoing message to `d`.

Returns

Type	Description
Gamma	The outgoing EP message to the `exp` argument.

Remarks

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

LogAverageFactor(Gamma, Gaussian, Gaussian)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Gamma exp, Gaussian d, Gaussian to_d)

Parameters

Type	Name	Description
Gamma	exp	Incoming message from `exp`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	d	Incoming message from `d`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	to_d	Previous outgoing message to `d`.

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_(exp,d) p(exp,d) factor(exp,d)).

Exceptions

Type	Condition
ImproperMessageException	`exp` is not a proper distribution.
ImproperMessageException	`d` is not a proper distribution.

LogEvidenceRatio(Gamma, Gaussian, Gaussian, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Gamma exp, Gaussian d, Gaussian to_d, Gamma to_exp)

Parameters

Type	Name	Description
Gamma	exp	Incoming message from `exp`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	d	Incoming message from `d`. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	to_d	Previous outgoing message to `d`.
Gamma	to_exp	Previous outgoing message to `exp`.

Returns

Type	Description
Double	Logarithm of the factor's contribution the EP model evidence.

Remarks

The formula for the result is log(sum_(exp,d) p(exp,d) factor(exp,d) / sum_exp p(exp) messageTo(exp)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

Exceptions

Type	Condition
ImproperMessageException	`exp` is not a proper distribution.
ImproperMessageException	`d` is not a proper distribution.