Class PlusTruncatedGaussianOp

Provides outgoing messages for the following factors:

, given random arguments to the function.

Inheritance

Object

PlusTruncatedGaussianOp

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)]
[FactorMethod(new string[]{"A", "Sum", "B"}, typeof(Factor), "Difference", new Type[]{typeof(double), typeof(double)}, Default = true)]
[Quality(QualityBand.Mature)]
public static class PlusTruncatedGaussianOp

Methods

AAverageConditional(Gaussian, Gaussian)

EP message to difference.

Declaration

public static TruncatedGaussian AAverageConditional(Gaussian sum, Gaussian b)

Parameters

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

Returns

Type	Description
TruncatedGaussian	The outgoing EP message to the `difference` argument.

Remarks

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

Exceptions

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

AAverageConditional(Gaussian, TruncatedGaussian)

EP message to difference.

Declaration

public static Gaussian AAverageConditional(Gaussian sum, TruncatedGaussian b)

Parameters

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

Returns

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

Remarks

Exceptions

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

AAverageConditional(TruncatedGaussian, Double)

EP message to difference.

Declaration

public static TruncatedGaussian AAverageConditional(TruncatedGaussian sum, double b)

Parameters

Type	Name	Description
TruncatedGaussian	sum	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
TruncatedGaussian	The outgoing EP message to the `difference` argument.

Remarks

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

Exceptions

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

AAverageConditional(Double, TruncatedGaussian)

EP message to difference.

Declaration

public static TruncatedGaussian AAverageConditional(double sum, TruncatedGaussian b)

Parameters

Type	Name	Description
Double	sum	Constant value for `a`.
TruncatedGaussian	b	Incoming message from `b`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

Type	Description
TruncatedGaussian	The outgoing EP message to the `difference` argument.

Remarks

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

Exceptions

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

BAverageConditional(Gaussian, Gaussian)

EP message to b.

Declaration

public static TruncatedGaussian BAverageConditional(Gaussian sum, Gaussian a)

Parameters

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

Returns

Type	Description
TruncatedGaussian	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,difference) p(a,difference) factor(difference,a,b)]/p(b).

Exceptions

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

BAverageConditional(Gaussian, TruncatedGaussian)

EP message to b.

Declaration

public static Gaussian BAverageConditional(Gaussian sum, TruncatedGaussian a)

Parameters

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

Returns

Type	Description
Gaussian	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,difference) p(a,difference) factor(difference,a,b)]/p(b).

Exceptions

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

BAverageConditional(TruncatedGaussian, Double)

EP message to b.

Declaration

public static TruncatedGaussian BAverageConditional(TruncatedGaussian sum, double a)

Parameters

Type	Name	Description
TruncatedGaussian	sum	Incoming message from `a`. Must be a proper distribution. If uniform, the result will be uniform.
Double	a	Constant value for `difference`.

Returns

Type	Description
TruncatedGaussian	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(difference,a,b)]/p(b).

Exceptions

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

BAverageConditional(Double, TruncatedGaussian)

EP message to b.

Declaration

public static TruncatedGaussian BAverageConditional(double sum, TruncatedGaussian a)

Parameters

Type	Name	Description
Double	sum	Constant value for `a`.
TruncatedGaussian	a	Incoming message from `difference`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

Type	Description
TruncatedGaussian	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_(difference) p(difference) factor(difference,a,b)]/p(b).

Exceptions

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

LogAverageFactor(TruncatedGaussian, Double, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(TruncatedGaussian sum, double a, double b)

Parameters

Type	Name	Description
TruncatedGaussian	sum	Incoming message from `a`. Must be a proper distribution. If uniform, the result will be uniform.
Double	a	Constant value for `difference`.
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(difference,a,b)).

Exceptions

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

LogAverageFactor(Double, TruncatedGaussian, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double sum, TruncatedGaussian a, double b)

Parameters

Type	Name	Description
Double	sum	Constant value for `a`.
TruncatedGaussian	a	Incoming message from `difference`. Must be a proper distribution. If uniform, the result will be uniform.
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_(difference) p(difference) factor(difference,a,b)).

Exceptions

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

LogAverageFactor(Double, Double, TruncatedGaussian)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double sum, double a, TruncatedGaussian b)

Parameters

Type	Name	Description
Double	sum	Constant value for `a`.
Double	a	Constant value for `difference`.
TruncatedGaussian	b	Incoming message from `b`. Must be a proper distribution. If uniform, the result will be uniform.

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(difference,a,b)).

Exceptions

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

SumAverageConditional(Gaussian, TruncatedGaussian)

EP message to a.

Declaration

public static Gaussian SumAverageConditional(Gaussian a, TruncatedGaussian b)

Parameters

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

Returns

Type	Description
Gaussian	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_(difference,b) p(difference,b) factor(difference,a,b)]/p(a).

Exceptions

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

SumAverageConditional(TruncatedGaussian, Gaussian)

EP message to a.

Declaration

public static Gaussian SumAverageConditional(TruncatedGaussian a, Gaussian b)

Parameters

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

Returns

Type	Description
Gaussian	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_(difference,b) p(difference,b) factor(difference,a,b)]/p(a).

Exceptions

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

SumAverageConditional(TruncatedGaussian, Double)

EP message to a.

Declaration

public static TruncatedGaussian SumAverageConditional(TruncatedGaussian a, double b)

Parameters

Type	Name	Description
TruncatedGaussian	a	Incoming message from `difference`. Must be a proper distribution. If uniform, the result will be uniform.
Double	b	Constant value for `b`.

Returns

Type	Description
TruncatedGaussian	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_(difference) p(difference) factor(difference,a,b)]/p(a).

Exceptions

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

SumAverageConditional(Double, TruncatedGaussian)

EP message to a.

Declaration

public static TruncatedGaussian SumAverageConditional(double a, TruncatedGaussian b)

Parameters

Type	Name	Description
Double	a	Constant value for `difference`.
TruncatedGaussian	b	Incoming message from `b`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

Type	Description
TruncatedGaussian	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(difference,a,b)]/p(a).

Exceptions

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