Model Fitting

Model fitting is the process of determining the values of parameters of a statistical model that are optimal for a given data set. Statistical models are used to describe and explain observed data using unknown parameters. Models are fit to the data by estimating the values of these unknown parameters that lead to the best possible description of the data. In this article, I briefly summarize the history and core concepts relevant to modern model fitting approaches including frequentist and Bayesian methods. I then describe new developments and debates on the topic. Additional reading materials are provided as well.