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)
A "GaussianGaussian" object.
Additional arguments to be passed to other inherited types.
numeric vector, the MPE estimate of "mu".
Gelman, Andrew, et al. Bayesian data analysis. CRC press, 2013.
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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)
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