marginalLikelihood.GaussianGaussian: Marginal likelihood of a "GaussianGaussian" object

In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

Description

Generate the marginal likelihood of the following model 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.
Marginal likelihood = p(x|m,S,Sigma)

Usage

## S3 method for class 'GaussianGaussian'
marginalLikelihood(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

numeric, the marginal likelihood.

References

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

See Also

GaussianGaussian, marginalLikelihood_bySufficientStatistics.GaussianGaussian

Examples

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))
marginalLikelihood(obj = obj,x=x,LOG = TRUE)
marginalLikelihood(obj = obj,x=x,LOG = FALSE)

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