MPE.GaussianNIW

<p>Additional arguments to be passed to other inherited types.</p>

<p>Generate the MPE of "theta" in following GaussianNIW structure: 
     theta|gamma ~ NIW(gamma) 
     x|theta ~ Gaussian(theta) 
where theta = (mu,Sigma) is the Gaussian parameter, gamma = (m,k,v,S) is the Normal-Inverse-Wishart(NIW) parameter. 
The model structure and prior parameters are stored in a "GaussianNIW" object. 
MPE is theta_MPE = mean_theta(theta|gamma,x)</p>

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.

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

Description

Usage

Arguments

Value

References

See Also