Class BetaFromMeanAndTotalCountOp

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

Inheritance

Object

BetaFromMeanAndTotalCountOp

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), "BetaFromMeanAndTotalCount", new Type[]{})]
[Quality(QualityBand.Experimental)]
public static class BetaFromMeanAndTotalCountOp

Fields

damping

How much damping to use to avoid improper messages. A higher value implies more damping.

Declaration

public static double damping

Field Value

Type	Description
Double

Methods

AverageLogFactor(Double, Double, Double)

Evidence message for VMP.

Declaration

public static double AverageLogFactor(double prob, double mean, double totalCount)

Parameters

Type	Name	Description
Double	prob	Constant value for prob.
Double	mean	Constant value for mean.
Double	totalCount	Constant value for totalCount.

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

LogAverageFactor(Beta, Double, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(Beta prob, double mean, double totalCount)

Parameters

Type	Name	Description
Beta	prob	Incoming message from prob.
Double	mean	Constant value for mean.
Double	totalCount	Constant value for totalCount.

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_(prob) p(prob) factor(prob,mean,totalCount)).

LogAverageFactor(Double, Double, Double)

Evidence message for EP.

Declaration

public static double LogAverageFactor(double prob, double mean, double totalCount)

Parameters

Type	Name	Description
Double	prob	Constant value for prob.
Double	mean	Constant value for mean.
Double	totalCount	Constant value for totalCount.

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(prob,mean,totalCount)).

MeanAverageConditional(Beta, Gamma, Beta, Beta)

EP message to mean.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Beta MeanAverageConditional(Beta mean, Gamma totalCount, Beta prob, Beta result)

Parameters

Type	Name	Description
Beta	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	totalCount	Incoming message from totalCount. Must be a proper distribution. If uniform, the result will be uniform.
Beta	prob	Incoming message from prob. Must be a proper distribution. If uniform, the result will be uniform.
Beta	result	Modified to contain the outgoing message.

Returns

Type	Description
Beta	result

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_(totalCount,prob) p(totalCount,prob) factor(prob,mean,totalCount)]/p(mean).

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.
ImproperMessageException	totalCount is not a proper distribution.
ImproperMessageException	prob is not a proper distribution.

MeanAverageConditional(Beta, Gamma, Double, Beta)

EP message to mean.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Beta MeanAverageConditional(Beta mean, Gamma totalCount, double prob, Beta result)

Parameters

Type	Name	Description
Beta	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	totalCount	Incoming message from totalCount. Must be a proper distribution. If uniform, the result will be uniform.
Double	prob	Constant value for prob.
Beta	result	Modified to contain the outgoing message.

Returns

Type	Description
Beta	result

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_(totalCount) p(totalCount) factor(prob,mean,totalCount)]/p(mean).

Exceptions

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

MeanAverageConditional(Beta, Double, Beta, Beta)

EP message to mean.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Beta MeanAverageConditional(Beta mean, double totalCount, Beta prob, Beta result)

Parameters

Type	Name	Description
Beta	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Double	totalCount	Constant value for totalCount.
Beta	prob	Incoming message from prob. Must be a proper distribution. If uniform, the result will be uniform.
Beta	result	Modified to contain the outgoing message.

Returns

Type	Description
Beta	result

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_(prob) p(prob) factor(prob,mean,totalCount)]/p(mean).

Exceptions

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

MeanAverageConditional(Beta, Double, Double, Beta)

EP message to mean.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Beta MeanAverageConditional(Beta mean, double totalCount, double prob, Beta result)

Parameters

Type	Name	Description
Beta	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Double	totalCount	Constant value for totalCount.
Double	prob	Constant value for prob.
Beta	result	Modified to contain the outgoing message.

Returns

Type	Description
Beta	result

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

MeanAverageLogarithm(Beta, Beta, Gamma, Beta)

VMP message to mean.

Declaration

public static Beta MeanAverageLogarithm(Beta prob, Beta mean, Gamma totalCount, Beta to_mean)

Parameters

Type	Name	Description
Beta	prob	Incoming message from prob. Must be a proper distribution. If uniform, the result will be uniform.
Beta	mean	Incoming message from mean.
Gamma	totalCount	Incoming message from totalCount. Must be a proper distribution. If uniform, the result will be uniform.
Beta	to_mean	Previous outgoing message to mean.

Returns

Type	Description
Beta	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_(prob,totalCount) p(prob,totalCount) log(factor(prob,mean,totalCount))).

Exceptions

Type	Condition
ImproperMessageException	prob is not a proper distribution.
ImproperMessageException	totalCount is not a proper distribution.

MeanAverageLogarithm(Double, Beta, Gamma, Beta)

VMP message to mean.

Declaration

public static Beta MeanAverageLogarithm(double prob, Beta mean, Gamma totalCount, Beta to_mean)

Parameters

Type	Name	Description
Double	prob	Constant value for prob.
Beta	mean	Incoming message from mean.
Gamma	totalCount	Incoming message from totalCount. Must be a proper distribution. If uniform, the result will be uniform.
Beta	to_mean	Previous outgoing message to mean.

Returns

Type	Description
Beta	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_(totalCount) p(totalCount) log(factor(prob,mean,totalCount))).

Exceptions

Type	Condition
ImproperMessageException	totalCount is not a proper distribution.

MeanAverageLogarithm(Double, Beta, Double, Beta)

VMP message to mean.

Declaration

public static Beta MeanAverageLogarithm(double prob, Beta mean, double totalCount, Beta to_mean)

Parameters

Type	Name	Description
Double	prob	Constant value for prob.
Beta	mean	Incoming message from mean.
Double	totalCount	Constant value for totalCount.
Beta	to_mean	Previous outgoing message to mean.

Returns

Type	Description
Beta	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.

ProbAverageConditional(Beta, Gamma)

EP message to prob.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Beta ProbAverageConditional(Beta mean, Gamma totalCount)

Parameters

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

Returns

Type	Description
Beta	The outgoing EP message to the prob argument.

Remarks

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

Exceptions

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

ProbAverageConditional(Beta, Double)

EP message to prob.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Beta ProbAverageConditional(Beta mean, double totalCount)

Parameters

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

Returns

Type	Description
Beta	The outgoing EP message to the prob argument.

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.

ProbAverageConditional(Double, Gamma)

EP message to prob.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Beta ProbAverageConditional(double mean, Gamma totalCount)

Parameters

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

Returns

Type	Description
Beta	The outgoing EP message to the prob argument.

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	totalCount is not a proper distribution.

ProbAverageConditional(Double, Double)

EP message to prob.

Declaration

public static Beta ProbAverageConditional(double mean, double totalCount)

Parameters

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

Returns

Type	Description
Beta	The outgoing EP message to the prob argument.

Remarks

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

ProbAverageLogarithm(Beta, Gamma)

VMP message to prob.

Declaration

public static Beta ProbAverageLogarithm(Beta mean, Gamma totalCount)

Parameters

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

Returns

Type	Description
Beta	The outgoing VMP message to the prob argument.

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	totalCount is not a proper distribution.

ProbAverageLogarithm(Beta, Double)

VMP message to prob.

Declaration

public static Beta ProbAverageLogarithm(Beta mean, double totalCount)

Parameters

Type	Name	Description
Beta	mean	Incoming message from mean.
Double	totalCount	Constant value for totalCount.

Returns

Type	Description
Beta	The outgoing VMP message to the prob argument.

Remarks

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

ProbAverageLogarithm(Double, Gamma)

VMP message to prob.

Declaration

public static Beta ProbAverageLogarithm(double mean, Gamma totalCount)

Parameters

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

Returns

Type	Description
Beta	The outgoing VMP message to the prob argument.

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	totalCount is not a proper distribution.

TotalCountAverageConditional(Beta, Gamma, Beta, Gamma)

EP message to totalCount.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Gamma TotalCountAverageConditional(Beta mean, Gamma totalCount, Beta prob, Gamma result)

Parameters

Type	Name	Description
Beta	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	totalCount	Incoming message from totalCount. Must be a proper distribution. If uniform, the result will be uniform.
Beta	prob	Incoming message from prob. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	result	Modified to contain the outgoing message.

Returns

Type	Description
Gamma	result

Remarks

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

Exceptions

Type	Condition
ImproperMessageException	mean is not a proper distribution.
ImproperMessageException	totalCount is not a proper distribution.
ImproperMessageException	prob is not a proper distribution.

TotalCountAverageConditional(Beta, Gamma, Double, Gamma)

EP message to totalCount.

Declaration

[NotSupported("Expectation Propagation does not currently support beta distributions with stochastic arguments.")]
public static Gamma TotalCountAverageConditional(Beta mean, Gamma totalCount, double prob, Gamma result)

Parameters

Type	Name	Description
Beta	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	totalCount	Incoming message from totalCount. Must be a proper distribution. If uniform, the result will be uniform.
Double	prob	Constant value for prob.
Gamma	result	Modified to contain the outgoing message.

Returns

Type	Description
Gamma	result

Remarks

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

Exceptions

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

TotalCountAverageLogarithm(Beta, Gamma, Beta, Gamma)

VMP message to totalCount.

Declaration

public static Gamma TotalCountAverageLogarithm(Beta mean, Gamma totalCount, Beta prob, Gamma to_totalCount)

Parameters

Type	Name	Description
Beta	mean	Incoming message from mean. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	totalCount	Incoming message from totalCount.
Beta	prob	Incoming message from prob. Must be a proper distribution. If uniform, the result will be uniform.
Gamma	to_totalCount	Previous outgoing message to totalCount.

Returns

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

Remarks

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

Exceptions

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