function kern = convolveKernParamInit(kern)

% RBFARD2KERNPARAMINIT RBFARD2 kernel parameter initialisation.
%
%	Description:
%	
%	
%	k_d,d'(x_i, x_j) = s_d*s_d'* N(x_i|x_j,P_d+P_d'+Lambda);
%	
%	The parameters are sigma2, the process variance (kern.variance), the
%	diagonal matrix of input scales (kern.inputScales, constrained to be
%	positive).
%	
%	
%
%	KERN = RBFARD2KERNPARAMINIT(KERN) initialises the automatic
%	relevance determination radial basis function kernel structure with
%	some default parameters.
%	 Returns:
%	  KERN - the kernel structure with the default parameters placed in.
%	 Arguments:
%	  KERN - the kernel structure which requires initialisation.
%	
%	
%
%	See also
%	RBFKERNPARAMINIT, KERNCREATE, KERNPARAMINIT


%	Copyright (c) 2004, 2005, 2006 Neil D. Lawrence
%	Copyright (c) 2009 Michalis K. Titsias
% 	rbfard2KernParamInit.m SVN version 582
% 	last update 2011-06-16T07:23:44.000000Z


% This parameter is restricted positive.
% S_d
kern.S=1*ones(kern.outputDimension,1);
kern.S=[1;-1];
% P_d we assume the P_d in different subscript _d are the same 
kern.P_d=1*ones(kern.outputDimension,kern.inputDimension);
% kern.P_d = [1,100;1,0.1;];
kern.P_d = [1,100,1,0.1];
% Lambda_k
kern.Lambda_k=1*ones(1,kern.inputDimension);
kern.nParams = kern.outputDimension +kern.inputDimension+kern.outputDimension*kern.inputDimension;

kern.transforms(1).index = [kern.outputDimension+1:kern.nParams];
kern.transforms(1).type = optimiDefaultConstraint('positive');

kern.isStationary = true;