bbricks (version 0.1.4)

rPosteriorPredictive.GaussianGaussian: Generate random samples from the posterior predictive distribution of a "GaussianGaussian" object

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 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

# NOT RUN {
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)
# }