Class BernoulliFromBetaOp

Provides outgoing messages for the following factors:

, given random arguments to the function.

Inheritance

Object

BernoulliFromBetaOp

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(Bernoulli), "Sample", new Type[]{typeof(double)})]
[FactorMethod(new string[]{"Sample", "ProbTrue"}, typeof(Factor), "Bernoulli", new Type[]{})]
[Quality(QualityBand.Mature)]
public static class BernoulliFromBetaOp

Methods

AverageLogFactor(Bernoulli, Beta)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(Bernoulli sample, Beta probTrue)

Parameters

Type	Name	Description
Bernoulli	sample	Incoming message from `sample`.
Beta	probTrue	Incoming message from `probTrue`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

Type	Description
Double	Average of the factor's log-value across the given argument distributions.

Remarks

The formula for the result is sum_(sample,probTrue) p(sample,probTrue) log(factor(sample,probTrue)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

Exceptions

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

AverageLogFactor(Bernoulli, Double)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(Bernoulli sample, double probTrue)

Parameters

Type	Name	Description
Bernoulli	sample	Incoming message from `sample`.
Double	probTrue	Constant value for `probTrue`.

Returns

Type	Description
Double	Average of the factor's log-value across the given argument distributions.

Remarks

The formula for the result is sum_(sample) p(sample) log(factor(sample,probTrue)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

AverageLogFactor(Boolean, Beta)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(bool sample, Beta probTrue)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.
Beta	probTrue	Incoming message from `probTrue`. Must be a proper distribution. If uniform, the result will be uniform.

Returns

Type	Description
Double	Average of the factor's log-value across the given argument distributions.

Remarks

The formula for the result is sum_(probTrue) p(probTrue) log(factor(sample,probTrue)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

Exceptions

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

AverageLogFactor(Boolean, Double)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(bool sample, double probTrue)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.
Double	probTrue	Constant value for `probTrue`.

Returns

Type	Description
Double	Average of the factor's log-value across the given argument distributions.

Remarks

The formula for the result is log(factor(sample,probTrue)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

LogAverageFactor(Bernoulli, Bernoulli)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Bernoulli sample, Bernoulli to_sample)

Parameters

Type	Name	Description
Bernoulli	sample	Incoming message from `sample`.
Bernoulli	to_sample	Outgoing message to `sample`.

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_(sample) p(sample) factor(sample,probTrue)).

LogAverageFactor(Boolean, Beta)

Evidence message for EP.

Declaration

public static double LogAverageFactor(bool sample, Beta probTrue)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.
Beta	probTrue	Incoming message from `probTrue`.

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_(probTrue) p(probTrue) factor(sample,probTrue)).

LogAverageFactor(Boolean, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(bool sample, double probTrue)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.
Double	probTrue	Constant value for `probTrue`.

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(sample,probTrue)).

LogEvidenceRatio(Bernoulli, Beta)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Bernoulli sample, Beta probTrue)

Parameters

Type	Name	Description
Bernoulli	sample	Incoming message from `sample`.
Beta	probTrue	Incoming message from `probTrue`.

Returns

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

Remarks

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

LogEvidenceRatio(Bernoulli, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Bernoulli sample, double probTrue)

Parameters

Type	Name	Description
Bernoulli	sample	Incoming message from `sample`.
Double	probTrue	Constant value for `probTrue`.

Returns

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

Remarks

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

LogEvidenceRatio(Boolean, Beta)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(bool sample, Beta probTrue)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.
Beta	probTrue	Incoming message from `probTrue`.

Returns

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

Remarks

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

LogEvidenceRatio(Boolean, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(bool sample, double probTrue)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.
Double	probTrue	Constant value for `probTrue`.

Returns

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

Remarks

The formula for the result is log(factor(sample,probTrue)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

ProbTrueAverageConditional(Bernoulli, Beta)

EP message to probTrue.

Declaration

public static Beta ProbTrueAverageConditional(Bernoulli sample, Beta probTrue)

Parameters

Type	Name	Description
Bernoulli	sample	Incoming message from `sample`. Must be a proper distribution. If uniform, the result will be uniform.
Beta	probTrue	Incoming message from `probTrue`.

Returns

Type	Description
Beta	The outgoing EP message to the `probTrue` argument.

Remarks

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

Exceptions

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

ProbTrueAverageConditional(Bernoulli, Double)

Declaration

public static Beta ProbTrueAverageConditional(Bernoulli sample, double probTrue)

Parameters

Type	Name	Description
Bernoulli	sample
Double	probTrue

Returns

Type	Description
Beta

ProbTrueAverageConditional(Boolean)

EP message to probTrue.

Declaration

public static Beta ProbTrueAverageConditional(bool sample)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.

Returns

Type	Description
Beta	The outgoing EP message to the `probTrue` argument.

Remarks

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

ProbTrueAverageLogarithm(Bernoulli)

VMP message to probTrue.

Declaration

public static Beta ProbTrueAverageLogarithm(Bernoulli sample)

Parameters

Type	Name	Description
Bernoulli	sample	Incoming message from `sample`.

Returns

Type	Description
Beta	The outgoing VMP message to the `probTrue` argument.

Remarks

The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except probTrue. The formula is exp(sum_(sample) p(sample) log(factor(sample,probTrue))).

ProbTrueAverageLogarithm(Boolean)

VMP message to probTrue.

Declaration

public static Beta ProbTrueAverageLogarithm(bool sample)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.

Returns

Type	Description
Beta	The outgoing VMP message to the `probTrue` argument.

Remarks

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

ProbTrueConditional(Boolean)

Gibbs message to probTrue.

Declaration

public static Beta ProbTrueConditional(bool sample)

Parameters

Type	Name	Description
Boolean	sample	Constant value for `sample`.

Returns

Type	Description
Beta	The outgoing Gibbs message to the `probTrue` argument.

Remarks

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

SampleAverageConditional(Beta)

EP message to sample.

Declaration

public static Bernoulli SampleAverageConditional(Beta probTrue)

Parameters

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

Returns

Type	Description
Bernoulli	The outgoing EP message to the `sample` argument.

Remarks

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

Exceptions

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

SampleAverageConditional(Double)

EP message to sample.

Declaration

public static Bernoulli SampleAverageConditional(double probTrue)

Parameters

Type	Name	Description
Double	probTrue	Constant value for `probTrue`.

Returns

Type	Description
Bernoulli	The outgoing EP message to the `sample` argument.

Remarks

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

SampleAverageLogarithm(Beta)

VMP message to sample.

Declaration

public static Bernoulli SampleAverageLogarithm(Beta probTrue)

Parameters

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

Returns

Type	Description
Bernoulli	The outgoing VMP message to the `sample` argument.

Remarks

The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except sample. The formula is exp(sum_(probTrue) p(probTrue) log(factor(sample,probTrue))).

Exceptions

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

SampleAverageLogarithm(Double)

VMP message to sample.

Declaration

public static Bernoulli SampleAverageLogarithm(double probTrue)

Parameters

Type	Name	Description
Double	probTrue	Constant value for `probTrue`.

Returns

Type	Description
Bernoulli	The outgoing VMP message to the `sample` argument.

Remarks

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

SampleConditional(Double)

Gibbs message to sample.

Declaration

public static Bernoulli SampleConditional(double probTrue)

Parameters

Type	Name	Description
Double	probTrue	Constant value for `probTrue`.

Returns

Type	Description
Bernoulli	The outgoing Gibbs message to the `sample` argument.

Remarks

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