sufficientStatistics_Weighted.CatDP: Weighted sufficient statistics of a "CatDP" object

Description

For following model structure: $$pi|alpha \sim DP(alpha,U)$$ $$x|pi \sim Categorical(pi)$$ where DP(alpha,U) is a Dirichlet Process on positive integers, alpha is the "concentration parameter" of the Dirichlet Process, U is the "base measure" of this Dirichlet process, it is an uniform distribution on all positive integers.Categorical() is the Categorical distribution. See dCategorical for the definition of the Categorical distribution. In the case of CatDP, x can only be positive integers. The model structure and prior parameters are stored in a "CatDP" object. The sufficient statistics of a set of samples x is:

unique positive integer values
effective counts of the unique positive integers

Usage

# S3 method for CatDP
sufficientStatistics_Weighted(obj, x, w, foreach = FALSE, ...)

Arguments

obj

A "CatDP" object.

integer, the elements of the vector must all greater than 0, the samples of a Categorical distribution.

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 "ssCatDP", the sufficient statistics of a set of categorical samples. Or an integer vector same as x if foreach=TRUE.

References

Teh, Yee W., et al. "Sharing clusters among related groups: Hierarchical Dirichlet processes." Advances in neural information processing systems. 2005.

Examples

Run this code

# NOT RUN {
x <- sample(1L:10L,size = 4,replace = TRUE)
obj <- CatDP()
w <- runif(4)
## return an object of class "ssCatDP" contains the weighted counts of each unique integer
sufficientStatistics_Weighted(obj=obj,x=x,w=w)
## return x itself, no matter what w is
sufficientStatistics_Weighted(obj=obj,x=x,w=w,foreach = TRUE)
# }