sufficientStatistics.CatDP {bbricks} | R Documentation |

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

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

`obj` |
A "CatDP" object. |

`x` |
integer, the elements of the vector must all greater than 0, the samples of a 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. |

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

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

`CatDP`

, `sufficientStatistics_Weighted.CatDP`

x <- sample(1L:10L,size = 4,replace = TRUE) obj <- CatDP() ## an object of class "ssCatDP", which contains the counts of each unique integer sufficientStatistics(obj=obj,x=x) ## will return x itself sufficientStatistics(obj=obj,x=x,foreach = TRUE)

[Package *bbricks* version 0.1.4 Index]