MPE.GaussianGaussian: Mean Posterior Estimate (MPE) of a "GaussianGaussian" object

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

Description

Generate the MPE estimate of mu in 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.
The MPE estimates is:

• mu_MPE = E(mu|m,S,x,Sigma)

Usage

## S3 method for class 'GaussianGaussian'
MPE(obj, ...)

Arguments

obj	A "GaussianGaussian" object.
...	Additional arguments to be passed to other inherited types.

Value

numeric vector, the MPE estimate of "mu".

References

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

See Also

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))
ss <- sufficientStatistics(obj=obj,x=x,foreach = FALSE)
## update prior into posterior
posterior(obj = obj,ss = ss)
## get the MPE estimate of mu
MPE(obj)

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