| marginalLikelihood.GaussianInvWishart | R Documentation |
Generate the marginal likelihood of the following model 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 model structure and prior parameters are stored in a "GaussianInvWishart" object.
Marginal likelihood = p(x|v,S,mu)
## S3 method for class 'GaussianInvWishart' marginalLikelihood(obj, x, LOG = TRUE, ...)
obj |
A "GaussianInvWishart" object. |
x |
matrix, or the ones that can be converted to matrix, each row of x is an observation. |
LOG |
Return the log density if set to "TRUE". |
... |
Additional arguments to be passed to other inherited types. |
numeric, the marginal likelihood.
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.
GaussianInvWishart, marginalLikelihood_bySufficientStatistics.GaussianInvWishart
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)) xNew <- 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) ## update piror with x posterior(obj=obj,ss = ss) ## use the posterior to calculate the likelihood of xNew marginalLikelihood(obj = obj,x = xNew,LOG = TRUE)