Class GaussianOpBase

Provides outgoing messages for the following factors:

• Sample(Double, Double)
• Gaussian(Double, Double)

, given random arguments to the function.

Inheritance

Object

GaussianOpBase

GaussianOp

GaussianOp_EM

GaussianOp_Laplace

GaussianOp_PointPrecision

GaussianOp_Slow

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

public class GaussianOpBase

Methods

LogAverageFactor(Gaussian, Gaussian, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Gaussian sample, Gaussian mean, double precision)

Parameters

Type	Name	Description
Gaussian	sample	Incoming message from sample. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Double	precision	Constant value for precision.

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,mean) p(sample,mean) factor(sample,mean,precision)).

Exceptions

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

LogAverageFactor(Gaussian, Double, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Gaussian sample, double mean, double precision)

Parameters

Type	Name	Description
Gaussian	sample	Incoming message from sample. Must be a proper distribution. If uniform, the result will be uniform.
Double	mean	Constant value for mean.
Double	precision	Constant value for precision.

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,mean,precision)).

Exceptions

Type	Condition
ImproperMessageException	sample is not a proper distribution.

LogAverageFactor(Double, Gaussian, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double sample, Gaussian mean, double precision)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Gaussian	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Double	precision	Constant value for precision.

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_(mean) p(mean) factor(sample,mean,precision)).

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

LogAverageFactor(Double, Double, Gamma)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double sample, double mean, Gamma precision)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	mean	Constant value for mean.
Gamma	precision	Incoming message from precision. 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_(precision) p(precision) factor(sample,mean,precision)).

Exceptions

Type	Condition
ImproperMessageException	precision is not a proper distribution.

LogAverageFactor(Double, Double, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double sample, double mean, double precision)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	mean	Constant value for mean.
Double	precision	Constant value for precision.

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,mean,precision)).

LogEvidenceRatio(Gaussian, Gaussian, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Gaussian sample, Gaussian mean, double precision)

Parameters

Type	Name	Description
Gaussian	sample	Incoming message from sample.
Gaussian	mean	Incoming message from mean.
Double	precision	Constant value for precision.

Returns

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

Remarks

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

LogEvidenceRatio(Gaussian, Double, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Gaussian sample, double mean, double precision)

Parameters

Type	Name	Description
Gaussian	sample	Incoming message from sample.
Double	mean	Constant value for mean.
Double	precision	Constant value for precision.

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,mean,precision) / sum_sample p(sample) messageTo(sample)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Double, Gaussian, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double sample, Gaussian mean, double precision)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Gaussian	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Double	precision	Constant value for precision.

Returns

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

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

LogEvidenceRatio(Double, Double, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double sample, double mean, Gamma precision)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	mean	Constant value for mean.
Gamma	precision	Incoming message from precision. Must be a proper distribution. If uniform, the result will be uniform.

Returns

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

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	precision is not a proper distribution.

LogEvidenceRatio(Double, Double, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double sample, double mean, double precision)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	mean	Constant value for mean.
Double	precision	Constant value for precision.

Returns

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

Remarks

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

MeanAverageConditional(Gaussian, Double)

EP message to mean.

Declaration

public static Gaussian MeanAverageConditional(Gaussian sample, double precision)

Parameters

Type	Name	Description
Gaussian	sample	Incoming message from sample. Must be a proper distribution. If uniform, the result will be uniform.
Double	precision	Constant value for precision.

Returns

Type	Description
Gaussian	The outgoing EP message to the mean argument.

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	sample is not a proper distribution.

MeanAverageConditional(Double, Double)

EP message to mean.

Declaration

public static Gaussian MeanAverageConditional(double sample, double precision)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	precision	Constant value for precision.

Returns

Type	Description
Gaussian	The outgoing EP message to the mean argument.

Remarks

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

PrecisionAverageConditional(Double, Double)

EP message to precision.

Declaration

public static Gamma PrecisionAverageConditional(double sample, double mean)

Parameters

Type	Name	Description
Double	sample	Constant value for sample.
Double	mean	Constant value for mean.

Returns

Type	Description
Gamma	The outgoing EP message to the precision argument.

Remarks

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

SampleAverageConditional(Gaussian, Double)

EP message to sample.

Declaration

public static Gaussian SampleAverageConditional(Gaussian mean, double precision)

Parameters

Type	Name	Description
Gaussian	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Double	precision	Constant value for precision.

Returns

Type	Description
Gaussian	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_(mean) p(mean) factor(sample,mean,precision)]/p(sample).

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

SampleAverageConditional(Double, Double)

EP message to sample.

Declaration

public static Gaussian SampleAverageConditional(double mean, double precision)

Parameters

Type	Name	Description
Double	mean	Constant value for mean.
Double	precision	Constant value for precision.

Returns

Type	Description
Gaussian	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.

TPdfLn(Double, Double, Double)

Logarithm of Student T density.

Declaration

public static double TPdfLn(double x, double v, double n)

Parameters

Type	Name	Description
Double	x	sample
Double	v	variance parameter
Double	n	degrees of freedom plus 1

Returns

Type	Description
Double