| sufficientStatistics.GaussianInvWishart | R Documentation |
For 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 sufficient statistics of a set of samples x (each row of x is a sample) are:
the effective number of samples N=nrow(x)
the centered sample scatter matrix S = (t(x)-mu)^T
## S3 method for class 'GaussianInvWishart' sufficientStatistics(obj, x, foreach = FALSE, ...)
obj |
A "GaussianInvWishart" object. |
x |
matrix, Gaussian samples, when x is a matrix, each row is a sample of dimension ncol(x). when x is a vector, x is length(x) samples of dimension 1. |
foreach |
logical, specifying whether to return the sufficient statistics for each observation. Default FALSE. |
... |
Additional arguments to be passed to other inherited types. |
If foreach=TRUE, will return a list of sufficient statistics for each row of x, otherwise will return the sufficient statistics of x as a whole.
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, sufficientStatistics_Weighted.GaussianInvWishart
obj <- GaussianInvWishart(gamma=list(mu=c(-1.5,1.5),v=3,S=diag(2))) x <- rGaussian(10,mu = c(-1.5,1.5),Sigma = matrix(c(0.1,0.03,0.03,0.1),2,2)) sufficientStatistics(obj=obj,x=x,foreach = FALSE) sufficientStatistics(obj=obj,x=x,foreach = TRUE)