Class GaussianOp_Slow

Provides outgoing messages for the following factors:

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

, given random arguments to the function.

Inheritance

Object

GaussianOpBase

GaussianOp_Slow

Inherited Members

GaussianOpBase.SampleAverageConditional(Double, Double)

GaussianOpBase.MeanAverageConditional(Double, Double)

GaussianOpBase.PrecisionAverageConditional(Double, Double)

GaussianOpBase.SampleAverageConditional(Gaussian, Double)

GaussianOpBase.MeanAverageConditional(Gaussian, Double)

GaussianOpBase.LogAverageFactor(Double, Double, Double)

GaussianOpBase.LogAverageFactor(Gaussian, Gaussian, Double)

GaussianOpBase.LogAverageFactor(Gaussian, Double, Double)

GaussianOpBase.LogAverageFactor(Double, Gaussian, Double)

GaussianOpBase.LogAverageFactor(Double, Double, Gamma)

GaussianOpBase.TPdfLn(Double, Double, Double)

GaussianOpBase.LogEvidenceRatio(Double, Double, Double)

GaussianOpBase.LogEvidenceRatio(Gaussian, Gaussian, Double)

GaussianOpBase.LogEvidenceRatio(Gaussian, Double, Double)

GaussianOpBase.LogEvidenceRatio(Double, Gaussian, Double)

GaussianOpBase.LogEvidenceRatio(Double, Double, Gamma)

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(Gaussian), "Sample", new Type[]{typeof(double), typeof(double)}, Default = false)]
[FactorMethod(new string[]{"sample", "mean", "precision"}, typeof(Factor), "Gaussian", new Type[]{}, Default = false)]
[Quality(QualityBand.Experimental)]
public class GaussianOp_Slow : GaussianOpBase

Fields

QuadratureNodeCount

Declaration

public static int QuadratureNodeCount

Field Value

Type	Description
Int32

Methods

FindZeroes(Func<Double, Double>, Func<Double, Double>, IList<Double>, IList<Double>)

Find all zeroes of a function, given its stationary points and inflection points

Declaration

public static List<double> FindZeroes(Func<double, double> func, Func<double, double> deriv, IList<double> stationaryPoints, IList<double> inflectionPoints)

Parameters

Type	Name	Description
Func<Double, Double>	func
Func<Double, Double>	deriv
IList<Double>	stationaryPoints
IList<Double>	inflectionPoints

Returns

Type	Description
List<Double>

GetIntegrationBoundsForPrecision(Double, Double, Double, Double, out Double, out Double, out Double)

Declaration

public static void GetIntegrationBoundsForPrecision(double m, double v, double a, double b, out double logrmin, out double logrmax, out double logrmode)

Parameters

Type	Name	Description
Double	m
Double	v
Double	a
Double	b
Double	logrmin
Double	logrmax
Double	logrmode

GetRealRoots(IList<Double>, out List<Double>, Predicate<Double>)

Get the real roots of a polynomial

Declaration

public static void GetRealRoots(IList<double> coeffs, out List<double> roots, Predicate<double> filter = null)

Parameters

Type	Name	Description
IList<Double>	coeffs	Coefficients of the polynomial, starting from the highest degree monomial down to the constant term
List<Double>	roots	On exit, the real roots
Predicate<Double>	filter	If not null, only roots where filter returns true are included

GetRoots(IList<Double>, out Double[], out Double[])

Get the complex roots of a polynomial

Declaration

public static void GetRoots(IList<double> coeffs, out double[] rootsReal, out double[] rootsImag)

Parameters

Type	Name	Description
IList<Double>	coeffs	Coefficients of the polynomial, starting from the highest degree monomial down to the constant term
Double[]	rootsReal	On exit, the real part of the roots
Double[]	rootsImag	On exit, the imaginary part of the roots

LogAverageFactor(Gaussian, Gaussian, Gamma)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Gaussian sample, Gaussian mean, Gamma 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.
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_(sample,mean,precision) p(sample,mean,precision) factor(sample,mean,precision)).

Exceptions

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

LogEvidenceRatio(Gaussian, Gaussian, Gamma, Gaussian)

Evidence message for EP.

Declaration

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

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.
Gamma	precision	Incoming message from precision. Must be a proper distribution. If uniform, the result will be uniform.
Gaussian	to_sample	Outgoing message to sample.

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

Exceptions

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

LogEvidenceRatio(Double, Gaussian, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(double sample, Gaussian mean, Gamma 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.
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_(mean,precision) p(mean,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	mean is not a proper distribution.
ImproperMessageException	precision is not a proper distribution.

MeanAverageConditional(Gaussian, Gaussian, Gamma)

EP message to mean.

Declaration

public static Gaussian MeanAverageConditional(Gaussian sample, Gaussian mean, Gamma 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.
Gamma	precision	Incoming message from precision. Must be a proper distribution. If uniform, the result will be uniform.

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

Exceptions

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

PrecisionAverageConditional(Gaussian, Gaussian, Gamma)

EP message to precision.

Declaration

public static Gamma PrecisionAverageConditional(Gaussian sample, Gaussian mean, Gamma 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.
Gamma	precision	Incoming message from precision. Must be a proper distribution. If uniform, the result will be uniform.

Returns

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

Remarks

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

Exceptions

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

SampleAverageConditional(Gaussian, Gaussian, Gamma)

EP message to sample.

Declaration

public static Gaussian SampleAverageConditional(Gaussian sample, Gaussian mean, Gamma precision)

Parameters

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

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

Exceptions

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