Class BooleanNotOp

Provides outgoing messages for Not(Boolean), given random arguments to the function.

Inheritance

Object

BooleanNotOp

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), "Not", new Type[]{})]
[Quality(QualityBand.Mature)]
public static class BooleanNotOp

Methods

AverageLogFactor()

Evidence message for VMP.

Declaration

public static double AverageLogFactor()

Returns

Type	Description
Double	Zero.

Remarks

The formula for the result is log(factor(not,b)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

AverageLogFactor(Boolean, Boolean)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(bool not, bool b)

Parameters

Type	Name	Description
Boolean	not	Constant value for not.
Boolean	b	Constant value for b.

Returns

Type	Description
Double	Zero.

Remarks

The formula for the result is log(factor(not,b)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

BAverageConditional(Bernoulli)

EP message to b.

Declaration

public static Bernoulli BAverageConditional(Bernoulli not)

Parameters

Type	Name	Description
Bernoulli	not	Incoming message from not. Must be a proper distribution. If uniform, the result will be uniform.

Returns

Type	Description
Bernoulli	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_(not) p(not) factor(not,b)]/p(b).

Exceptions

Type	Condition
ImproperMessageException	not is not a proper distribution.

BAverageConditional(Boolean)

EP message to b.

Declaration

public static Bernoulli BAverageConditional(bool not)

Parameters

Type	Name	Description
Boolean	not	Constant value for not.

Returns

Type	Description
Bernoulli	The outgoing EP message to the b argument.

Remarks

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

BAverageLogarithm(Bernoulli)

VMP message to b.

Declaration

public static Bernoulli BAverageLogarithm(Bernoulli not)

Parameters

Type	Name	Description
Bernoulli	not	Incoming message from not. Must be a proper distribution. If uniform, the result will be uniform.

Returns

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

Remarks

The outgoing message is the factor viewed as a function of b with not integrated out. The formula is sum_not p(not) factor(not,b).

Exceptions

Type	Condition
ImproperMessageException	not is not a proper distribution.

BAverageLogarithm(Boolean)

VMP message to b.

Declaration

public static Bernoulli BAverageLogarithm(bool not)

Parameters

Type	Name	Description
Boolean	not	Constant value for not.

Returns

Type	Description
Bernoulli	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.

LogAverageFactor(Bernoulli, Bernoulli, Bernoulli)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Bernoulli not, Bernoulli b, Bernoulli to_not)

Parameters

Type	Name	Description
Bernoulli	not	Incoming message from not.
Bernoulli	b	Incoming message from b.
Bernoulli	to_not	Outgoing message to not.

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_(not,b) p(not,b) factor(not,b)).

LogAverageFactor(Bernoulli, Boolean)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Bernoulli not, bool b)

Parameters

Type	Name	Description
Bernoulli	not	Incoming message from not.
Boolean	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_(not) p(not) factor(not,b)).

LogAverageFactor(Boolean, Bernoulli)

Evidence message for EP.

Declaration

public static double LogAverageFactor(bool not, Bernoulli b)

Parameters

Type	Name	Description
Boolean	not	Constant value for not.
Bernoulli	b	Incoming message from 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_(b) p(b) factor(not,b)).

LogAverageFactor(Boolean, Boolean)

Evidence message for EP.

Declaration

public static double LogAverageFactor(bool not, bool b)

Parameters

Type	Name	Description
Boolean	not	Constant value for not.
Boolean	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(factor(not,b)).

LogEvidenceRatio(Bernoulli)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Bernoulli not)

Parameters

Type	Name	Description
Bernoulli	not	Incoming message from not.

Returns

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

Remarks

The formula for the result is log(sum_(not) p(not) factor(not,b) / sum_not p(not) messageTo(not)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Boolean, Bernoulli)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(bool not, Bernoulli b)

Parameters

Type	Name	Description
Boolean	not	Constant value for not.
Bernoulli	b	Incoming message from b.

Returns

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

Remarks

The formula for the result is log(sum_(b) p(b) factor(not,b)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Boolean, Boolean)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(bool not, bool b)

Parameters

Type	Name	Description
Boolean	not	Constant value for not.
Boolean	b	Constant value for b.

Returns

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

Remarks

The formula for the result is log(factor(not,b)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

NotAverageConditional(Bernoulli)

EP message to not.

Declaration

public static Bernoulli NotAverageConditional(Bernoulli b)

Parameters

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

Returns

Type	Description
Bernoulli	The outgoing EP message to the not argument.

Remarks

The outgoing message is a distribution matching the moments of not as the random arguments are varied. The formula is proj[p(not) sum_(b) p(b) factor(not,b)]/p(not).

Exceptions

Type	Condition
ImproperMessageException	b is not a proper distribution.

NotAverageLogarithm(Bernoulli)

VMP message to not.

Declaration

public static Bernoulli NotAverageLogarithm(Bernoulli b)

Parameters

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

Returns

Type	Description
Bernoulli	The outgoing VMP message to the not argument.

Remarks

The outgoing message is a distribution matching the moments of not as the random arguments are varied. The formula is proj[sum_(b) p(b) factor(not,b)].

Exceptions

Type	Condition
ImproperMessageException	b is not a proper distribution.