1. IE 300, Fall 2012 Richard Sowers IESE

2. 8/30/2012 Goals: Rules of Probability Counting Equally likely Some examples

3. 9/4/2012 Goals Conditional Probability Model-building

4. 9/6/2012 Independence – Definition – Model-building – Examples

5. 9/11/2012 Bayes’ Rule – Implicit conditional probabilities Random variables (if possible)

6. 9/13/2012 Discrete random variables – Examples Uniform Geometric Binomial Bernoulli – Probability mass functions Expectations

7. 9/27/2012 Expectations and variances – Direct computations – Moment generating functions (not in book)

8. 9/18/2012 Expectations – Means – Variances – Moment generating functions Several random variables – Binomial – Geometric – Bernoulli – Poisson – Uniform

9. 9/20/2012 Review

10. 9/28/2012 Expectations and Variances – Bernoulli – Binomial – Law of large numbers

11. 10/2/2012 Moment generating function – Bernoulli – Binomial – Geometric – Negative Binomial – Poisson Begin continuous random variables

12. 10/4/2012 Continuous random variables Properties of densities Exponential random variables

13. 10/9/2012 Poisson connection to exponential random variables Gaussian random variables

14. 10/11/2012 Gaussian random variables Limit theorems

15. 10/18/2012 Joint random variables Joint probability mass function Conditioning Marginals

16. 10/23/2012 More joint random variables Joint probability mass function Conditioning Marginals

17. 11/1/2012 More on transformations of random variables Joint Gaussians

18. 11/6/2012 Statistical analysis of data Sample mean, sample variance, population variance Histograms and frequency plots

19. 11/13/2012 Estimation of parameters – Bernoulli – Exponential – Gaussian

20. 11/15/2012 Hypothesis testing (9-5)