sufficientStatistics_Weighted.CatDP {bbricks} R Documentation

## 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 class 'CatDP'
sufficientStatistics_Weighted(obj, x, w, foreach = FALSE, ...)
```

### Arguments

 `obj` A "CatDP" object. `x` integer, the elements of the vector must all greater than 0, the samples of a 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 "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.

`CatDP`, `sufficientStatistics.CatDP`

### Examples

```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)
```

[Package bbricks version 0.1.4 Index]