marginalLikelihood.CatDP

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

Return the log density if set to "TRUE".

Additional arguments to be passed to other inherited types.

Generate the marginal likelihood of the following model structure: 
 pi|alpha ~ DP(alpha,U) 
 x|pi ~ 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. 
In the case of CatDP, x can only be positive integers. 
The model structure and prior parameters are stored in a "CatDP" object. 
Marginal likelihood is the likelihood of x|alpha.

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.

marginalLikelihood.CatDP: Marginal likelihood of a "CatDP" object

Description

Usage

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See Also