function [L,gam]=loglios(th,params,Hk,k,scl,blurs)
% [L,gam]=LOGLIOS(th,params,Hk,k,scl,blurs)
%
% Calculates the full negative logarithmic likelihood and its
% derivatives, i.e. minus LKOS and minus GAMMAKOS averaged over
% wavenumber space. This is the function that we need to MINIMIZE!
%
% INPUT:
%
% th       The five-parameter vector argument [scaled]:
%          th(1)=D    Isotropic flexural rigidity 
%          th(2)=f2   The sub-surface to surface initial loading ratio 
%          th(3)=s2   The first Matern parameter, aka sigma^2 
%          th(4)=nu   The second Matern parameter 
%          th(5)=rho  The third Matern parameter 
% params   A structure with AT LEAST these constants that are known:
%          DEL   surface and subsurface density contrast [kg/m^3]
%          g     gravitational acceleration [m/s^2]
% Hk       A complex matrix of Fourier-domain observations
% k        the wavenumbers at which these are being evaluated [1/m]
% scl      The vector with any scalings applied to the parameter vector
% blurs    1 Blur using the spectral window [default]
%          0 Don't blur using the spectral window
%
% OUTPUT:
%
% L        The loglihood, averaged over all wavenumbers
% gam      The score, averaged over all wavenumbers
%
% SEE ALSO:
%
% FISHERKOS, which should be incorporated at a later stage
%
% Last modified by fjsimons-at-alum.mit.edu, 4/21/2011

% Default scaling is none
defval('scl',ones(size(th)))

defval('blurs',1)

% Scale up the parameter vector for the proper likelihood and score
th=th.*scl;

% The NaN's will be in here at zero wavenumber
% Note: should we restrict this to the upper halfplane? or will mean do
% Get the likelihood at the individual wavenumbers; average
L=-nanmean(Lkos(k,th,params,Hk,blurs));

% Get the scores at the individual wavenumbers; average
%gam=-nanmean(gammakos(k,th,params,Hk));
% The correct gradient is too heterogeneous to be good so scale
%gam=gam.*scl;
gam=NaN;

% Print the trajectory, seems like one element at a time gets changed
% disp(sprintf('Current theta: %8.3g %8.3g %8.3g %8.3g %8.3g',th))