bbricks (version 0.1.4)

dPosteriorPredictive.GaussianGaussian: Posterior predictive density function of a "GaussianGaussian" object

Description

Generate the the density value of 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 density is p(x|m,S,Sigma).

Usage

# S3 method for GaussianGaussian
dPosteriorPredictive(obj, x, LOG = TRUE, ...)

Arguments

obj

A "GaussianGaussian" object.

x

matrix, or the ones that can be converted to matrix, each row of x is an observation.

LOG

Return the log density if set to "TRUE".

...

Additional arguments to be passed to other inherited types.

Value

A numeric vector of the same length as nrow(x), the posterior predictive density.

References

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

See Also

GaussianGaussian, dPosteriorPredictive.GaussianGaussian, marginalLikelihood.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)))
x <- rGaussian(100,c(0,0),Sigma = matrix(c(2,1,1,2),2,2))
dPosteriorPredictive(obj = obj,x=x,LOG = TRUE)
dPosteriorPredictive(obj = obj,x=x,LOG = FALSE)
# }