sample_from_gaussian_process_prior_i - Samples from the prior of a Gaussian process with independent dimensions


#include "gp/gp_gaussian_processes.h"

Example compile flags (system dependent):
   -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 sample_from_gaussian_process_prior_i
	Vector_vector **sample,
	const Vector_vector *indices,
	int (*mean_func)(Vector **,const Vector *,int),
	int (*cov_func)(Matrix **,const Vector *,const Vector *,const void *,int),
	const void *hyper_params,
	int d


This routine samples from the prior distribution of a Gaussian process, whose mean function and covariance function are given by mean_func and cov_func, respectively, and whose dimensions are independent. The sample will be in the indices given by the vectors of indices, and will each be of dimension d. Naturally, the vectors of indices must all be of equal length. The library provides a few widely-used mean and covariance functions. Also, sample will have indices->length vectors, each of length d. If the vector pointed to by sample is NULL, then a vector of the appropriate size is created. If it exists, but is the wrong size, then it is recreated. Otherwise, the storage is recycled.


If the routine fails (due to storage allocation, an error in the mean or covariance functions, or a mismatch in the sizes of the indices), then ERROR is returned with and error message being set. Otherwise NO_ERROR is returned.


squared_exponential_covariance_function, zero_mean_function, sample_from_gaussian_process_prior


This software is not adequatedly tested. It is recomended that results are checked independantly where appropriate.


Ernesto Brau


Ernesto Brau


fill_covariance_matrix , fill_mean_vector , sample_from_gaussian_process_prior , 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_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