I've a question about code design for a numerical optimization of the likelihood function of the sort:
LL = LogLik(x,y,Param,L,Ps,P)
My objective function makes use of the multivariate normal probability density in the following fashion:
eta = mvnpdf(y,x*Phi,Sigma);
Where sigma is passed throug the parameter vector 'Param' (together with Phi). I then try to numerically optimize that through fmincon but then Sigma shoud be a positive-definite covariance matrix, condition that is not always defined and causes an error.
Results = fmincon(@(Param)LogLik(X,Y1,Param,L,Ps,P),x0,,,Aeq',beq,,,,options)
I'm asking your help as I suspect this is not the right approach to solve the problem at hand.