Class DifferenceBetaOp
Provides outgoing messages for Difference(Double, Double), given random arguments to the function.
Inherited Members
Namespace: Microsoft.ML.Probabilistic.Factors
Assembly: Microsoft.ML.Probabilistic.dll
Syntax
[FactorMethod(typeof(Factor), "Difference", new Type[]{typeof(double), typeof(double)})]
[Quality(QualityBand.Experimental)]
public static class DifferenceBetaOp
Methods
AAverageConditional(Gaussian, Beta)
EP message to a
.
Declaration
public static Beta AAverageConditional(Gaussian difference, Beta b)
Parameters
Type | Name | Description |
---|---|---|
Gaussian | difference | Incoming message from |
Beta | b | Incoming message from |
Returns
Type | Description |
---|---|
Beta | The outgoing EP message to the |
Remarks
The outgoing message is a distribution matching the moments of a
as the random arguments are varied. The formula is proj[p(a) sum_(difference,b) p(difference,b) factor(difference,a,b)]/p(a)
.
Exceptions
Type | Condition |
---|---|
ImproperMessageException |
|
BAverageConditional(Gaussian, Beta)
EP message to b
.
Declaration
public static Beta BAverageConditional(Gaussian difference, Beta a)
Parameters
Type | Name | Description |
---|---|---|
Gaussian | difference | Incoming message from |
Beta | a | Incoming message from |
Returns
Type | Description |
---|---|
Beta | The outgoing EP message to the |
Remarks
The outgoing message is a distribution matching the moments of b
as the random arguments are varied. The formula is proj[p(b) sum_(difference,a) p(difference,a) factor(difference,a,b)]/p(b)
.
Exceptions
Type | Condition |
---|---|
ImproperMessageException |
|
DifferenceAverageConditional(Beta, Beta)
EP message to difference
.
Declaration
public static Gaussian DifferenceAverageConditional(Beta a, Beta b)
Parameters
Type | Name | Description |
---|---|---|
Beta | a | Incoming message from |
Beta | b | Incoming message from |
Returns
Type | Description |
---|---|
Gaussian | The outgoing EP message to the |
Remarks
The outgoing message is a distribution matching the moments of difference
as the random arguments are varied. The formula is proj[p(difference) sum_(a,b) p(a,b) factor(difference,a,b)]/p(difference)
.