bbricks (version 0.1.4)

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

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 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

# 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))
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)
# }