rPosteriorPredictive.GaussianGaussian: 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(mu,Sigma)

mu \sim Gaussian(m,S)

Where Sigma is known. Gaussian() is the Gaussian distribution. See ?dGaussian for the definition of Gaussian distribution.
The model structure and prior parameters are stored in a "GaussianGaussian" object.
Posterior predictive is a distribution of x|m,S,Sigma.

Usage

## S3 method for class 'GaussianGaussian'
rPosteriorPredictive(obj, n = 1, ...)

Arguments

obj	A "GaussianGaussian" 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

Gelman, Andrew, et al. Bayesian data analysis. CRC press, 2013.

See Also

GaussianGaussian, dPosteriorPredictive.GaussianGaussian

Examples

obj <- GaussianGaussian(gamma=list(Sigma=matrix(c(2,1,1,2),2,2),m=c(0.2,0.5),S=diag(2)))
rPosteriorPredictive(obj=obj,20)

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