Lecture Notes

SES #	LECTURE NOTES	SUPPLEMENTAL MATERIALS
1	Conditional Probability, Bayes’ Rule and Independence (PDF)
2	Discrete Probability Models (PDF)
3	Continuous Probability Models I & II (PDF - 2.1MB)	Figures for lecture 3: Probability Models Applied to Data (PDF)
4	Joint Distributions and Independent Random Variables (PDF - 1.7MB)	Figures for Lecture 4: Multivariate Gaussian Distribution, Part 1 (PDF) Figures for Lecture 4: Multivariate Gaussian Distribution, Part 2 (PDF)
5	Conditional Distributions and Functions of Jointly Distributed Random Variables I & II (PDF - 1.7MB)
6	Moment Generating Functions I & II (PDF - 2.1MB)
7	The Law of Large Numbers and the Central Limit Theorem (PDF)	Lecture 7 Addendum: The Law of Large Numbers and the Central Limit Theorem, (PDF)
8	Method-of-Moments Estimation (PDF - 1.7MB)
9	Likelihood Theory I & II (PDF - 1.5MB)	Lecture 9 Addendum: The Observed Fisher Information and Fisher Information for the Binomial Model (PDF)
10.1	Bayesian Methods (PDF)
10.2	Corrigienda (PDF)	Figures for lecture 10: Bayesian Analyses, Beta Probability Models (PDF)
11	Bootstrap and Monte Carlo Methods (PDF - 1.4MB)
12	Hypothesis Testing I & II (PDF - 2.3MB)
13	Simple Regression Model I, II & III (PDF - 1.4MB)	Lecture 13 Addendum: The Meaning of Regression (PDF)
14	Analysis of Variance (PDF - 1.2MB)

Course Info