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- Lecture 13. F) Some Notes
- Lecture 13. G) Interval Estimation/Confidence Intervals
- Lecture 14. A) Convergence
- Lecture 14. B) Law of Large Numbers
- Lecture 14. C) Convergence in Distribution
- Lecture 14. D) Slutsky’s Theorem
- Lecture 14. E) Central Limit Theorem
- Lecture 14. F) Delta Method
- Lecture 14. G) Somewhat Pedantic Remark on Notation
- Lecture 15. A) Asymptotic Properties of ML Estimators
- Lecture 15. B) Some Implications
- Lecture 15. C) Example: Hypothesis Test
- Lecture 15. D) Example: Exponential Distribution
- Lecture 15. E) Multiple Parameters
- Lecture 16. A) Bayesian Inference
- Lecture 16. B) Example: Coin Tossing
- Lecture 16. C) A More General Example
- Lecture 16. D) Conjugate Priors
- Lecture 16. E) Normal Distribution
- Lecture 16. F) "Counterexample"
- Lecture 16. G) Multiple Observations
- Lecture 16. H) Theorem: Berstein von-Mises
- Lecture 17. A) Ordinary Least Squares
- Lecture 17. B) Normal Linear Model
- Lecture 17. C) Asymptotic Properties of OLS
- Lecture 17. D) Bootstrapping
- Lecture 18. A) Multicollinearity
- Lecture 18. B) Partitioned Regression
- Lecture 18. C) Gauss-Markov Theorem
- Lecture 2. A) Random Variables (cont.)
- Lecture 2. B) Leibniz Rule
- Lecture 2. B) Leibniz Rule II
- Lecture 2. C) Transformations of Random Variables
- Lecture 3. A) Expected Value
- Lecture 3. B) Moments
- Lecture 3. C) Moment Generating Function
- Lecture 4. A) Distributions
- Lecture 4. B) Bernoulli
- Lecture 4. C) Binomial
- Lecture 4. D) Poisson
- Lecture 4. E) Uniform
- Lecture 4. F) Gamma
- Lecture 4. G) Normal
- Lecture 4. H) Dirac delta function
- Lecture 5. A) Families of Distributions
- Lecture 5. B) Chebychev's Inequality
- Lecture 5. C) Multiple Random Variables
- Lecture 6. A) Multiple Random Variables (cont.)
- Lecture 6. B) Conditional PMF/PDF
- Lecture 6. C) Conditional Moments
