- Likelihood and Likelihood Function
- Likelihood of observing data
- Likelihhod as a function of parameters based single data value $x_i$: $L(\theta|x_i) \propto f(x_i|\theta)$ or $P(x_i|\theta)$
- Continous Distribution: $f(x_i|\theta)$
- Discrete Distribution: $P(x_i|\theta)$
- Two Parts of Likelihood Principle
- Law of likelihood: if $L(\theta_1|x > L(\theta|x))$, then $x$ supports $\theta_1$ over $\theta_2$
- All evidence about $\theta$ is contained in $L(\theta|x)$
- Maximum Likelihood Estimation with IID Data
- Maximize likelihood /log-likelihood
- Score equations (also called gradient)
- Notes and code
- Maximum likelihood estimation
[HTML]
| |
- Base R Functions for Finding MLE
- A Technical Tutorial on Optimization
[HTML]
Good for those who want to have a big-picture view of various practical optimization methods.
- Review Basic Rules of Rules
- Rules for general functions
[link]
- Rules for logarithmic and exponential functions
[link]
- Written Assignment
- Guidelines: [HTML]
- Due: Wednesday, 3/3/2026
|