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

## 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.

See Also

sufficientStatistics.CatDirichlet CatDirichlet

Examples

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

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