LinearGaussianGaussian {bbricks} | R Documentation |
Create an object of type "LinearGaussianGaussian", which represents the Linear Gaussian and Gaussian conjugate structure:
x \sim Gaussian(A z + b, Sigma)
z \sim Gaussian(m,S)
Where Sigma is known. A is a m x n matrix, x is a m x 1 random vector, z is a n x 1 random vector, b is a n x 1 vector. Gaussian() is the Gaussian distribution. See ?dGaussian
for the definition of Gaussian distribution.
The created 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.
LinearGaussianGaussian( objCopy = NULL, ENV = parent.frame(), gamma = list(Sigma = 1, m = 0, S = 1) )
objCopy |
an object of type "LinearGaussianGaussian". If "objCopy" is not NULL, the function create a new "LinearGaussianGaussian" object by copying the content from objCopy, otherwise this new object will be created by using "ENV" and "gamma". Default NULL. |
ENV |
environment, specify where the object will be created. |
gamma |
list, a named list of parameters, gamma=list(Sigma,m,S). Where gamma$Sigma is the known covariance matrix of x, gamma$m and gamma$S are the prior mean and covariance matrix of z. |
An object of class "LinearGaussianGaussian".
Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
posterior.LinearGaussianGaussian
,posteriorDiscard.LinearGaussianGaussian
,MAP.LinearGaussianGaussian
,MPE.LinearGaussianGaussian
,marginalLikelihood.LinearGaussianGaussian
,rPosteriorPredictive.LinearGaussianGaussian
,dPosteriorPredictive.LinearGaussianGaussian
.
obj <- LinearGaussianGaussian(gamma=list(Sigma=matrix(c(2,1,1,2),2,2), m=c(0.2,0.5,0.3),S=diag(3))) obj #print the content