rPosteriorPredictive.GaussianNIG: 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:

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 ?dInvGamma and dGaussian for the definitions of these distribution.
The model structure and prior parameters are stored in a "GaussianNIG" object.
Posterior predictive is a distribution of x|m,V,a,b,X

Usage

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

Arguments

obj	A "GaussianNIG" object.
n	integer, number of samples.
X	matrix, the location of the prediction, each row is a location.
...	Additional arguments to be passed to other inherited types.

Value

A matrix of n rows and nrow(X) columns, each row is a sample.

References

Banerjee, Sudipto. "Bayesian Linear Model: Gory Details." Downloaded from http://www. biostat. umn. edu/ ph7440 (2008).

See Also

GaussianNIG, dPosteriorPredictive.GaussianNIG

Examples

obj <- GaussianNIG(gamma=list(m=c(1,1),V=diag(2),a=1,b=1))
X <- matrix(runif(20),ncol=2)
rPosteriorPredictive(obj=obj,n=3,X=X)

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