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.
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