Resource:

Used to infer Model Parameters from collected data for example in Linear Regression ().

Definition: Likelihood

Why is it a good tool for guessing parameter values?

The likelihoods plot a distribution, the max gives the most likely.

This is called the MLE.

Properties of a MLE:

  • As more data comes in the Estimator should approach a true value
  • MLE is a consistent Estimator, i.e it gets closer to the true parameter value as the sample size grows.
  • Asymptotical Normal
  • Asymptotic Efficiency

Assumptions for MLE:

  • Regularity

parametric vs non-parametric models

Likelihood is a function of a parameter