MPE.CatDP: Mean Posterior Estimate(MPE) of a "CatDP" object

In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

Description

Generate the MPE estimate of "pi" in 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 MPE of pi is pi_MPE = E(pi|alpha,x), E() is the expectation function.

Usage

## S3 method for class 'CatDP'
MPE(obj, ...)

Arguments

obj	A "CatDP" object.
...	Additional arguments to be passed to other inherited types.

Value

numeric.

References

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

See Also

CatDP

Examples

x <- sample(1L:10L,size = 40,replace = TRUE)
obj <- CatDP()
posterior(obj = obj,ss = x)
MPE(obj)

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