ABSTRACT
Random sampling of the cost or likelihood functions is a powerful approach for nonlinear parameter estimation. The Metropolis algorithm is an efficient computational sampling method for the likelihood function. This chapter applies the Metropolis algorithm to an empirical model as a prelude to more advanced sampling techniques such as Markov Chain Monte Carlo parameter estimation.
