bbricks (version 0.1.4)

GaussianNIW: Create objects of type "GaussianNIW".

Description

Create an object of type "GaussianNIW", which represents the Gaussian-Normal-Inverse-Wishart (Gaussian-NIW) conjugate structure: $$mu,Sigma|m,k,v,S \sim NIW(m,k,v,S)$$ $$x|mu,Sigma \sim Gaussian(mu,Sigma)$$ Where NIW() is the Normal-Inverse-Wishart distribution, Gaussian() is the Gaussian distribution. See ?dNIW and dGaussian for the definitions of these distribution. This 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.

Usage

GaussianNIW(
  objCopy = NULL,
  ENV = parent.frame(),
  gamma = list(m = 0, k = 1, v = 2, S = 1)
)

Arguments

objCopy

An object of type "GaussianNIW". If "objCopy" is not NULL, the function create a new "GaussianNIW" object by copying the content from objCopy, otherwise this new object will be created by using "ENV" and "gamma". Default NULL.

ENV

environment, specify in which environment the object will be created.

gamma

list, a named list of NIW parameters, gamma=list(m,k,v,S). Where gamma$m is a numeric "location" parameter; gamma$S is a symmetric positive definite matrix representing the "scale" parameters; gamma$k and gamma$v are numeric values.

Value

An object of class "GaussianNIW".

References

Murphy, Kevin P. "Conjugate Bayesian analysis of the Gaussian distribution." def 1.22 (2007): 16.

Gelman, Andrew, et al. "Bayesian Data Analysis Chapman & Hall." CRC Texts in Statistical Science (2004).

See Also

posterior.GaussianNIW,posteriorDiscard.GaussianNIW,MAP.GaussianNIW,MPE.GaussianNIW,marginalLikelihood.GaussianNIW,rPosteriorPredictive.GaussianNIW,dPosteriorPredictive.GaussianNIW ...

Examples

# NOT RUN {
obj <- GaussianNIW(gamma=list(m=c(0,1),k=0.0001,v=2,S=diag(2)))
obj #print the content
# }