# sufficientStatistics_Weighted.CatDirichlet: Weighted sufficient statistics of a "CatDirichlet" object In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

## Description

For following Categorical-Dirichlet 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.
the sufficient statistics of a set of samples x and weights w are:
the effective counts (in this case the sum of the weight w) of each unique label in x
Unique values of x must be in obj\$gamma\$uniqueLabels, where "obj" is a "CatDirichlet" object, see examples below.

## Usage

 ```1 2``` ```## S3 method for class 'CatDirichlet' sufficientStatistics_Weighted(obj, x, w, foreach = FALSE, ...) ```

## Arguments

 `obj` A "CatDirichlet" object. `x` numeric,integer or character, samples of the Categorical distribution. `w` numeric, sample weights. `foreach` logical, specifying whether to return the sufficient statistics for each observation. Default FALSE. `...` Additional arguments to be passed to other inherited types.

## Value

An object of class "ssCat", the sufficient statistics of a set of categorical samples. Or an object of the same class as x if foreach=TRUE.

## References

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

`sufficientStatistics.CatDirichlet` `CatDirichlet`
 ```1 2 3 4 5``` ```obj <- CatDirichlet(gamma=list(alpha=runif(26,1,2),uniqueLabels = letters)) x <- sample(letters,size = 20,replace = TRUE) w <- runif(20) sufficientStatistics(obj=obj,x=x) #return the counts of each unique label sufficientStatistics_Weighted(obj=obj,x=x,w=w) #return the weighted counts of each unique lable ```