At the end of this course, the student will learn:
- Methods and theory of deriving likelihood functions
- Conditional and marginal likelihood functions
- Main statistical optimization methods such as EM algorithm
- Mixture distributions
- i.e. basic concepts that establishes one's power in
- Calculating asymptotic standard errors