sufficientStatistics_Weighted.DP

Random samples of the "BasicBayesian" object.

Additional arguments to be passed to other inherited types.

For following model structure: 
Create an object of type "DP", which represents the Dirichlet-Process model structure: 
 pi|alpha ~ DP(alpha,U) 
 z|pi ~ Categorical(pi) 
 theta_z|psi ~ H0(psi) 
 x|theta_z,z ~ F(theta_z) 
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. The choice of F() and H0() can be arbitrary, they are distributions of x and theta_z correspondingly. 
The sufficient statistics of a set of samples x in a "DP" object is the same sufficient statistics of the "BasicBayesian" inside the "DP", see examples.

A class of frequently used Bayesian parametric and nonparametric model structures, as well as a set of tools for common analytical tasks. Structures include Gaussian and Normal-Inverse-Wishart conjugate structure, Gaussian and Normal-Inverse-Gamma conjugate structure, Categorical and Dirichlet conjugate structure, Dirichlet Process on positive integers, Dirichlet Process in general, Hierarchical Dirichlet Process ... Tasks include updating posteriors, calculating marginal likelihood, calculating posterior predictive densities, sampling from posterior predictive distributions, calculating "Maximum A Posteriori" (MAP) estimates ... See Murphy (2012, <doi:10.1080/09332480.2014.914768>), Koller and Friedman (2009, <doi:10.1017/s0269888910000275>) and Andrieu, de Freitas, Doucet and Jordan (2003, <doi:10.1023/A:1020281327116>) for more information. See <https://chenhaotian.github.io/Bayesian-Bricks/> to get started.

Haotian Chen

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

Description

Usage

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References

See Also