Class ExpOp_Laplace

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

Inheritance

Object

ExpOp_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(Math), "Exp", new Type[]{typeof(double)})]
[Buffers(new string[]{"x"})]
[Quality(QualityBand.Experimental)]
public static class ExpOp_Laplace

Methods

DAverageConditional(Gamma, Gaussian, Double)

EP message to d.

Declaration

public static Gaussian DAverageConditional(Gamma exp, Gaussian d, double x)

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.
Double	x	Buffer `x`.

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, Double)

EP message to exp.

Declaration

public static Gamma ExpAverageConditional(Gamma exp, Gaussian d, double x)

Parameters

Type	Name	Description
Gamma	exp	Incoming message from `exp`.
Gaussian	d	Incoming message from `d`.
Double	x	Buffer `x`.

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, Double)

Evidence message for EP.

Declaration

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

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.
Double	x	Buffer `x`.

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, Double, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Gamma exp, Gaussian d, double x, 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.
Double	x	Buffer `x`.
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.

X(Gamma, Gaussian)

Update the buffer x.

Declaration

public static double X(Gamma exp, Gaussian 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.

Returns

Type	Description
Double	New value of buffer `x`.

Remarks

Exceptions

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

X2(Gamma, Gaussian, Double)

Declaration

public static double X2(Gamma exp, Gaussian d, double x)

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.
Double	x	Buffer `x`.

Returns

Type	Description
Double

Remarks

Exceptions

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

XInit(Gaussian)

Initialize the buffer x.

Declaration

public static double XInit(Gaussian d)

Parameters

Type	Name	Description
Gaussian	d	Incoming message from `d`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

Type	Description
Double	Initial value of buffer `x`.

Remarks

Exceptions

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