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