prior_gaussian_likelihood

PURPOSE ^

Parameters for image

SYNOPSIS ^

function likelihood= prior_gaussian_likelihood( inv_model, x, y, J )

DESCRIPTION ^

 Parameters for image
   inv_model.gaussian_prior_likelihood.img_mean -> image mean
   inv_model.gaussian_prior_likelihood.R_prior -> L*L' = inv(image covariance)
   inv_model.gaussian_prior_likelihood.img_exp -> ( default = 2)
 Parameters for data
   inv_model.gaussian_prior_likelihood.Noise -> L*L' = inv(Noise covariance)
   inv_model.gaussian_prior_likelihood.data_exp -> ( default = 2)

 Function to be used with inv_solve_mcmc

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function likelihood= prior_gaussian_likelihood( inv_model, x, y, J )
0002 % Parameters for image
0003 %   inv_model.gaussian_prior_likelihood.img_mean -> image mean
0004 %   inv_model.gaussian_prior_likelihood.R_prior -> L*L' = inv(image covariance)
0005 %   inv_model.gaussian_prior_likelihood.img_exp -> ( default = 2)
0006 % Parameters for data
0007 %   inv_model.gaussian_prior_likelihood.Noise -> L*L' = inv(Noise covariance)
0008 %   inv_model.gaussian_prior_likelihood.data_exp -> ( default = 2)
0009 %
0010 % Function to be used with inv_solve_mcmc
0011 
0012 % (C) 2007 Nick Polydorides. License: GPL version 2 or version 3
0013 % $Id: prior_gaussian_likelihood.m 3122 2012-06-08 15:49:01Z bgrychtol $
0014 
0015 x_m  = inv_model.gaussian_prior_likelihood.img_mean;
0016 L_x  = inv_model.gaussian_prior_likelihood.R_prior;
0017 p_x  = inv_model.gaussian_prior_likelihood.img_exp;
0018 L_n  = inv_model.gaussian_prior_likelihood.Noise;
0019 p_n  = inv_model.gaussian_prior_likelihood.data_exp;
0020 
0021 img_residual=  x - x_m;
0022 data_residual= y - J*x;
0023 
0024 likelihood= exp(- norm(L_n * data_residual, p_n) ...
0025                 - norm(L_x * img_residual,  p_x));

Generated on Wed 21-Jun-2017 09:29:07 by m2html © 2005