NAME

compute_gaussian_process_likelihood - Computes the likelihood of 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
(
	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.

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_i , compute_gaussian_process_marginal_likelihood , compute_gaussian_process_marginal_likelihood_i , compute_gaussian_process_marginal_log_likelihood , compute_gaussian_process_marginal_log_likelihood_i