rPosteriorPredictive.GaussianNIW: Generate random samples from the posterior predictive...

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

Description

Generate random samples from the posterior predictive distribution of the following structure:

mu,Sigma|m,k,v,S \sim NIW(m,k,v,S)

x|mu,Sigma \sim Gaussian(mu,Sigma)

Where NIW() is the Normal-Inverse-Wishart distribution, Gaussian() is the Gaussian distribution. See ?dNIW and dGaussian for the definitions of these distribution.
The model structure and prior parameters are stored in a "GaussianNIW" object.
Posterior predictive is a distribution of x|m,k,v,S.

Usage

## S3 method for class 'GaussianNIW'
rPosteriorPredictive(obj, n, ...)

Arguments

obj	A "GaussianNIW" object.
n	integer, number of samples.
...	Additional arguments to be passed to other inherited types.

Value

A matrix of n rows, each row is a sample.

References

Murphy, Kevin P. "Conjugate Bayesian analysis of the Gaussian distribution." def 1.22 (2007): 16.

Gelman, Andrew, et al. "Bayesian Data Analysis Chapman & Hall." CRC Texts in Statistical Science (2004).

See Also

GaussianNIW, dPosteriorPredictive.GaussianNIW

Examples

obj <- GaussianNIW(gamma=list(m=c(0,0),k=1,v=2,S=diag(2)))
rPosteriorPredictive(obj=obj,20)

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