gp_logPosterior: Compute a posterior density given likelihood and prior.

In svdataman/gin: Gaussian Processes for time series inference

Description

gp_logPosterior compute posterior density for GP model.

Usage

gp_logPosterior(theta, acv.model = NULL, tau = NULL, dat = NULL,
  PDcheck = TRUE, chatter = 0, logPrior = NULL)

Arguments

theta	(vector) parameters for covariance function the first element is the mean value mu
acv.model	(name) name of the function to compute ACV(tau | theta)
tau	(matrix) N*N matrix of lags at which to compute ACF
dat	(matrix) an N * 3 matrix of data: 3 columns
PDcheck	(logical) use Matrix::nearPD to coerse the matrix
chatter	(integer) higher values give more run-time feedback
logPrior	(name) Name of the function returning the log Prior density.

Details

Calculate posterior density for a Gaussian Process (GP) model given some data (day) and functions for the AutoCovariance (ACV) of the GP, the log likelihood, and the log prior (both log densities).

Simply computes log(posterior) = log(likelihood) + log(prior), where log(likelihood) is a function of the data and a ACV (and its parameters), and log(prior) is a function of the ACV parameters only.

Value

log posterior density (scalar)

See Also

gp_logLikelihood

svdataman/gin documentation built on March 12, 2021, 7:37 a.m.