Priors in the d2d framework

Prior knowledge about parameters can be considered as well. If knowledge is available, a distribution can be used as a penalisation term for individual parameters by setting

#!matlab

ar.type(jp)=1;

for a normal distributed penalisation term,

#!matlab

ar.type(jp)=2;

for a uniform distributed penalisation term with normal bounds and

#!matlab

ar.type(jp)=3;

for a L1 penalisation term.

For type 1 and 3, one needs to further specify the mean and the standard deviation of the required distribution. These parameters can be set at

#!matlab

ar.mean(jp)

and

#!matlab

ar.std(jp)

Utilizing priors as described enables Bayesian parameter estimation by maximizing the posterior.

For information on how priors enter the objective function of the parameter estimation process, consider the wiki-section about Objective function, likelihood and chi-square.