posterior.CatDirichlet: Update a "CatDirichlet" object with sample sufficient...

In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

Description

For the model structure:

pi|alpha \sim Dir(alpha)

x|pi \sim Categorical(pi)

Where Dir() is the Dirichlet distribution, Categorical() is the Categorical distribution. See ?dDir and dCategorical for the definitions of these distribution.
update alpha by adding the information of newly observed samples x.
The model structure and prior parameters are stored in a "CatDirichlet" object, the prior parameters in this object will be updated after running this function.

Usage

## S3 method for class 'CatDirichlet'
posterior(obj, ss, w = NULL, ...)

Arguments

obj	A "CatDirichlet" object.
ss	Sufficient statistics of x. In Categorical-Dirichlet case the sufficient statistic of sample x can be either x itself, of an "ssCat" object generated by the function sufficientStatistics.CatDirichlet().
w	Sample weights, default NULL.
...	Additional arguments to be passed to other inherited types.

Value

None. the gamma stored in "obj" will be updated based on "ss".

References

Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.

See Also

CatDirichlet,posteriorDiscard.CatDirichlet

Examples

obj <- CatDirichlet(gamma=list(alpha=rep(1,26),uniqueLabels = letters))
x <- sample(letters,size = 20,replace = TRUE)
w <- runif(20)
posterior(obj=obj,ss=x)
obj
posteriorDiscard(obj=obj,ss=x)
obj
## weighted sample
posterior(obj=obj,ss=x,w=w)
obj
posteriorDiscard(obj=obj,ss=x,w=w)
obj

bbricks documentation built on July 8, 2020, 7:29 p.m.