| rPosteriorPredictive.GaussianNIW | R Documentation |
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.
## S3 method for class 'GaussianNIW' rPosteriorPredictive(obj, n, ...)
obj |
A "GaussianNIW" object. |
n |
integer, number of samples. |
... |
Additional arguments to be passed to other inherited types. |
A matrix of n rows, each row is a sample.
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).
GaussianNIW, dPosteriorPredictive.GaussianNIW
obj <- GaussianNIW(gamma=list(m=c(0,0),k=1,v=2,S=diag(2))) rPosteriorPredictive(obj=obj,20)