Description Usage Arguments Value References See Also Examples
View source: R/Gaussian_Inference.r
Create an object of type "GaussianInvWishart", which represents the Gaussian and Inverse-Wishart conjugate structure:
x \sim Gaussian(mu,Sigma)
Sigma \sim InvWishart(v,S)
mu is known. Gaussian() is the Gaussian distribution. See ?dGaussian
and ?dInvWishart
for the definition of the distributions.
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.
1 2 3 4 5 | GaussianInvWishart(
objCopy = NULL,
ENV = parent.frame(),
gamma = list(mu = 0, v = 3, S = 1)
)
|
objCopy |
an object of type "GaussianInvWishart". If "objCopy" is not NULL, the function create a new "GaussianInvWishart" 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(mu,v,S). Where gamma$mu is the known mean vector of x, gamma$v and gamma$S are the prior degree of freedom and scale of Sigma. |
An object of class "GaussianInvWishart".
Gelman, Andrew, et al. Bayesian data analysis. CRC press, 2013.
posterior.GaussianInvWishart
,posteriorDiscard.GaussianInvWishart
,MAP.GaussianInvWishart
,MPE.GaussianInvWishart
,marginalLikelihood.GaussianInvWishart
,rPosteriorPredictive.GaussianInvWishart
,dPosteriorPredictive.GaussianInvWishart
...
1 2 | obj <- GaussianInvWishart(gamma=list(mu=c(-1.5,1.5),v=3,S=diag(2)))
obj #print the content
|
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