ABSTRACT
Cost functions provide an alternative approach to parameter estimation, which is derived from least-squares principles and optimization of the model-data residual. As a result, optimization of a cost function is similar to optimization of the log-likelihood function. This chapter introduces Bayes' Rule with cost and likelihood functions to account for additional prior information as a way to constrain the optimum parameter set for a model.
