(eng): The classical formulation of the linear inverse problem is studied from Bayesian point of view. We show that the classical regularization is equivalent to prior covariance matrix. We formulate several parametrization of the prior covariance matrix and derive estimation algorithms for them. The advantages of teh new algorithms are demonstrated on data from teh ETEX experiment.