sufficientStatistics.CatDirichlet: 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 is:

• the effective counts of each unique label in x. i.e. T(x)[i] = sum(uniqueLabels[i]

Usage

## S3 method for class 'CatDirichlet'
sufficientStatistics(obj, x, foreach = FALSE, ...)

Arguments

obj	A "CatDirichlet" object.
x	numeric,integer or character, samples of the Categorical distribution.
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

CatDirichlet, sufficientStatistics_Weighted.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
sufficientStatistics(obj=obj,x=x,foreach = TRUE) #return the sample itself

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