Class PoissonOp

Provides outgoing messages for Poisson(Double), given random arguments to the function.

Inheritance

Object

PoissonOp

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), "Poisson", new Type[]{typeof(double)})]
[Quality(QualityBand.Stable)]
public static class PoissonOp

Fields

ForceProper

Declaration

public static bool ForceProper

Field Value

Type	Description
Boolean

Methods

AverageLogFactor(Poisson, Gamma)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(Poisson sample, Gamma mean)

Parameters

Type	Name	Description
Poisson	sample	Incoming message from sample.
Gamma	mean	Incoming message from mean. 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,mean) p(sample,mean) log(factor(sample,mean)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

AverageLogFactor(Poisson, Double)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(Poisson sample, double mean)

Parameters

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

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,mean)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

AverageLogFactor(Int32, Gamma)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(int sample, Gamma mean)

Parameters

Type	Name	Description
Int32	sample	Constant value for sample.
Gamma	mean	Incoming message from mean. 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_(mean) p(mean) log(factor(sample,mean)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

AverageLogFactor(Int32, Double)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(int sample, double mean)

Parameters

Type	Name	Description
Int32	sample	Constant value for sample.
Double	mean	Constant value for mean.

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,mean)). Adding up these values across all factors and variables gives the log-evidence estimate for VMP.

LogAverageFactor(Poisson, Gamma)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Poisson sample, Gamma mean)

Parameters

Type	Name	Description
Poisson	sample	Incoming message from sample.
Gamma	mean	Incoming message from mean.

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)).

LogAverageFactor(Poisson, Double, Poisson)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Poisson sample, double mean, Poisson to_sample)

Parameters

Type	Name	Description
Poisson	sample	Incoming message from sample.
Double	mean	Constant value for mean.
Poisson	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,mean)).

LogAverageFactor(Int32, Gamma, Gamma)

Evidence message for EP.

Declaration

public static double LogAverageFactor(int sample, Gamma mean, Gamma to_mean)

Parameters

Type	Name	Description
Int32	sample	Constant value for sample.
Gamma	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	to_mean	Outgoing message to mean.

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)).

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

LogAverageFactor(Int32, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(int sample, double mean)

Parameters

Type	Name	Description
Int32	sample	Constant value for sample.
Double	mean	Constant value for mean.

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)).

LogEvidenceRatio(Poisson, Gamma, Poisson)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Poisson sample, Gamma mean, Poisson to_sample)

Parameters

Type	Name	Description
Poisson	sample	Incoming message from sample.
Gamma	mean	Incoming message from mean.
Poisson	to_sample	Previous 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) p(sample,mean) factor(sample,mean) / sum_sample p(sample) messageTo(sample)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Poisson, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(Poisson sample, double mean)

Parameters

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

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

LogEvidenceRatio(Int32, Gamma, Gamma)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(int sample, Gamma mean, Gamma to_mean)

Parameters

Type	Name	Description
Int32	sample	Constant value for sample.
Gamma	mean	Incoming message from mean.
Gamma	to_mean	Outgoing message to mean.

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)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

LogEvidenceRatio(Int32, Double)

Evidence message for EP.

Declaration

public static double LogEvidenceRatio(int sample, double mean)

Parameters

Type	Name	Description
Int32	sample	Constant value for sample.
Double	mean	Constant value for mean.

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)). Adding up these values across all factors and variables gives the log-evidence estimate for EP.

MeanAverageConditional(Poisson, Gamma)

EP message to mean.

Declaration

public static Gamma MeanAverageConditional(Poisson sample, Gamma mean)

Parameters

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

Returns

Type	Description
Gamma	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)]/p(mean).

Exceptions

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

MeanAverageConditional(Int32)

EP message to mean.

Declaration

public static Gamma MeanAverageConditional(int sample)

Parameters

Type	Name	Description
Int32	sample	Constant value for sample.

Returns

Type	Description
Gamma	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.

MeanAverageLogarithm(Poisson)

VMP message to mean.

Declaration

public static Gamma MeanAverageLogarithm(Poisson sample)

Parameters

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

Returns

Type	Description
Gamma	The outgoing VMP message to the mean argument.

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	sample is not a proper distribution.

MeanAverageLogarithm(Int32)

VMP message to mean.

Declaration

public static Gamma MeanAverageLogarithm(int sample)

Parameters

Type	Name	Description
Int32	sample	Constant value for sample.

Returns

Type	Description
Gamma	The outgoing VMP message to the mean argument.

Remarks

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

SampleAverageConditional(Poisson, Gamma)

EP message to sample.

Declaration

public static Poisson SampleAverageConditional(Poisson sample, Gamma mean)

Parameters

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

Returns

Type	Description
Poisson	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)]/p(sample).

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

SampleAverageConditional(Double)

EP message to sample.

Declaration

public static Poisson SampleAverageConditional(double mean)

Parameters

Type	Name	Description
Double	mean	Constant value for mean.

Returns

Type	Description
Poisson	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(Gamma)

VMP message to sample.

Declaration

public static Poisson SampleAverageLogarithm(Gamma mean)

Parameters

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

Returns

Type	Description
Poisson	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_(mean) p(mean) log(factor(sample,mean))).

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

SampleAverageLogarithm(Double)

VMP message to sample.

Declaration

public static Poisson SampleAverageLogarithm(double mean)

Parameters

Type	Name	Description
Double	mean	Constant value for mean.

Returns

Type	Description
Poisson	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.