dPosteriorPredictive.GaussianInvWishart {bbricks}R Documentation

Posterior predictive density function of a "GaussianInvWishart" object


Generate the the density value of 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 ?dGaussian and ?dInvWishart for the definition of the distributions.
The model structure and prior parameters are stored in a "GaussianInvWishart" object.
Posterior predictive density is p(x|v,S,mu).


## S3 method for class 'GaussianInvWishart'
dPosteriorPredictive(obj, x, LOG = TRUE, ...)



A "GaussianInvWishart" object.


matrix, or the ones that can be converted to matrix, each row of x is an observation.


Return the log density if set to "TRUE".


Additional arguments to be passed to other inherited types.


A numeric vector of the same length as nrow(x), the posterior predictive density.


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.

See Also

GaussianInvWishart, dPosteriorPredictive.GaussianInvWishart, marginalLikelihood.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 probability of observing each xNew
dPosteriorPredictive(obj = obj,x = xNew,LOG = TRUE)

[Package bbricks version 0.1.4 Index]