dPosteriorPredictive.LinearGaussianGaussian {bbricks}R Documentation

Posterior predictive density function of a "LinearGaussianGaussian" object


Generate the the density value of the posterior predictive distribution of the following structure:

x \sim Gaussian(A z + b, Sigma)

z \sim Gaussian(m,S)

Where Sigma is known. A is a dimx x dimz matrix, x is a dimx x 1 random vector, z is a dimz x 1 random vector, b is a dimm x 1 vector. Gaussian() is the Gaussian distribution. See ?dGaussian for the definition of Gaussian distribution.
The model structure and prior parameters are stored in a "LinearGaussianGaussian" object.
Posterior predictive density is p(x|m,S,A,b,Sigma).


## S3 method for class 'LinearGaussianGaussian'
dPosteriorPredictive(obj, x, A, b = NULL, LOG = TRUE, ...)



A "LinearGaussianGaussian" object.


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


matrix or list. when x is a N x 1 matrix, A must be a matrix of N x dimz, dimz is the dimension of z; When x is a N x dimx matrix, where dimx > 1, A can be either a list or a matrix. When A is a list, A = {A_1,A_2,...A_N} is a list of dimx x dimz matrices. If A is a single dimx x dimz matrix, it will be replicated N times into a length N list


matrix, when x is a N x 1 matrix, b must also be a N x 1 matrix or length N vector; When x is a N x dimx matrix, where dimx > 1, b can be either a matrix or a vector. When b is a matrix, b={b_1^T,...,b_N^T} is a N x dimx matrix, each row is a transposed vector. When b is a length dimx vector, it will be transposed into a row vector and replicated N times into a N x dimx matrix. When b = NULL, it will be treated as a vector of zeros. Default NULL.


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.


Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.

See Also

LinearGaussianGaussian, rPosteriorPredictive.LinearGaussianGaussian, marginalLikelihood.LinearGaussianGaussian


obj <- LinearGaussianGaussian(gamma=list(Sigma=matrix(c(2,1,1,2),2,2),
x <- rGaussian(100,mu = runif(2),Sigma = diag(2))
A <- matrix(runif(6),2,3)
b <- runif(2)
dPosteriorPredictive(obj = obj,x=x,A=A,b=b)

[Package bbricks version 0.1.4 Index]