MPE.GaussianNIG

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

<p>Generate the MPE estimate of (beta,sigma^2) in following Gaussian-NIG structure:
   $$x \sim Gaussian(X beta,sigma^2)$$
   $$sigma^2 \sim InvGamma(a,b)$$
   $$beta \sim Gaussian(m,sigma^2 V)$$
Where X is a row vector, or a design matrix where each row is an obervation. InvGamma() is the Inverse-Gamma distribution, Gaussian() is the Gaussian distribution. See <code>?dInvGamma</code> and <code>dGaussian</code> for the definitions of these distribution.
The model structure and prior parameters are stored in a "GaussianNIG" object. 
The MPEs are E(beta,sigma^2|m,V,a,b,X,x)</p>

A set of frequently used Bayesian parametric and nonparametric model structures, as well as a set of tools for common analytical tasks. Structures include linear Gaussian systems, 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, sampling from posteriors, calculating marginal likelihood, calculating posterior predictive densities, sampling from posterior predictive distributions, calculating "Maximum A Posteriori" (MAP) estimates ... See <https://chenhaotian.github.io/Bayesian-Bricks/> to get started.

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

Description

Usage

Arguments

Value

References

See Also