Generate random samples from the posterior predictive distribution of the following structure:
x \sim Gaussian(mu,Sigma)
Sigma \sim InvWishart(v,S)
mu is known. Gaussian() is the Gaussian distribution. See
?dInvWishart for the definition of the distributions.
The model structure and prior parameters are stored in a "GaussianInvWishart" object.
Posterior predictive is a distribution of x|v,S,mu.
A "GaussianInvWishart" object.
integer, number of samples.
Additional arguments to be passed to other inherited types.
A matrix of n rows, each row is a sample.
Gelman, Andrew, et al. Bayesian data analysis. CRC press, 2013.
MARolA, K. V., JT KBNT, and J. M. Bibly. Multivariate analysis. AcadeInic Press, Londres, 1979.
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obj <- GaussianInvWishart(gamma=list(mu=c(-1.5,1.5),v=3,S=diag(2))) x <- rGaussian(100,mu = c(-1.5,1.5),Sigma = matrix(c(0.1,0.03,0.03,0.1),2,2)) ss <- sufficientStatistics(obj=obj,x=x,foreach = FALSE) ## use x to update the prior informatoin posterior(obj=obj,ss = ss) ## use the posterior to generate new samples rPosteriorPredictive(obj = obj,n=20)
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