Parameter estimation is the problem of using data to estimate one or more unknown quantities in a model for the signal. This requires finding a function of the data that gives a good quality estimate of the parameter of interest. In this lesson you will learn about the two common criteria used to design estimators: the maximum likelihood criterion and the Bayesian approach. Understanding the principles behind these two popular approaches will prepare you to choose the best criterion for your parameter estimation applications.
[Content protected for Mastery, Pro, Professional members only. Please login or upgrade your membership to see this content.]