GaussianGaussian {bbricks} | R Documentation |
Create an object of type "GaussianGaussian", which represents the Gaussian and Gaussian conjugate 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 created object will be used as a place for recording and accumulating information in the related inference/sampling functions such as posterior(), posteriorDiscard(), MAP(), marginalLikelihood(), dPosteriorPredictive(), rPosteriorPredictive() and so on.
GaussianGaussian( objCopy = NULL, ENV = parent.frame(), gamma = list(Sigma = 1, m = 0, S = 1) )
objCopy |
an object of type "GaussianGaussian". If "objCopy" is not NULL, the function create a new "GaussianGaussian" object by copying the content from objCopy, otherwise this new object will be created by using "ENV" and "gamma". Default NULL. |
ENV |
environment, specify where the object will be created. |
gamma |
list, a named list of parameters, gamma=list(Sigma,m,S). Where gamma$Sigma is the known covariance matrix of x, gamma$m and gamma$S are the prior mean and covariance matrix of mu. |
An object of class "GaussianGaussian".
Gelman, Andrew, et al. Bayesian data analysis. CRC press, 2013.
posterior.GaussianGaussian
,posteriorDiscard.GaussianGaussian
,MAP.GaussianGaussian
,MPE.GaussianGaussian
,marginalLikelihood.GaussianGaussian
,rPosteriorPredictive.GaussianGaussian
,dPosteriorPredictive.GaussianGaussian
.
obj <- GaussianGaussian(gamma=list(Sigma=matrix(c(2,1,1,2),2,2),m=c(0.2,0.5),S=diag(2))) obj #print the content