NAME
compute_gaussian_process_likelihood_i - Computes the likelihood of a GP the data given parameters
SYNOPSIS
#include "gp/gp_gaussian_processes.h"
Example compile flags (system dependent):
-DLINUX_X86_64 -DLINUX_X86_64_OPTERON -DGNU_COMPILER
-I/home/kobus/include
-L/home/kobus/misc/load/linux_x86_64_opteron -L/usr/lib/x86_64-linux-gnu
-lKJB -lfftw3 -lgsl -lgslcblas -ljpeg -lSVM -lstdc++ -lpthread -lSLATEC -lg2c -lacml -lacml_mv -lblas -lg2c -lncursesw
int compute_gaussian_process_likelihood_i
(
double *density,
const Vector_vector *train_data,
const Vector_vector *function_values,
double noise_sigma
);
DESCRIPTION
This routine computes the likelihood of the data given the parameters. In the
context of Gaussian processes, this means computing the likelihood of the
training data (train_data) give some function values (function_values) at time
points train_indices. noise_sigma is the variance of the noise process. This
routine assumes that the dimensions of the data are (statistically) independent.
RETURNS
If the routine fails (due to storage allocation, an error in the
covariance function, or a mismatch in the sizes of the indices), then ERROR
is returned with and error message being set. Otherwise NO_ERROR is returned.
DISCLAIMER
This software is not adequatedly tested. It is recomended that
results are checked independantly where appropriate.
AUTHOR
Ernesto Brau
DOCUMENTER
Ernesto Brau
SEE ALSO
fill_covariance_matrix
,
fill_mean_vector
,
sample_from_gaussian_process_prior
,
sample_from_gaussian_process_prior_i
,
sample_from_gaussian_process_predictive
,
sample_from_gaussian_process_predictive_i
,
get_gaussian_process_predictive_distribution
,
get_gaussian_process_predictive_distribution_i
,
get_gaussian_process_posterior_distribution
,
get_gaussian_process_posterior_distribution_i
,
compute_gaussian_process_likelihood
,
compute_gaussian_process_marginal_likelihood
,
compute_gaussian_process_marginal_likelihood_i
,
compute_gaussian_process_marginal_log_likelihood
,
compute_gaussian_process_marginal_log_likelihood_i