Probabilistic Systems Analysis and Applied Probability

Lecture Notes

These slides can also be found in the Video Lectures section, in the Lecture Slides tab associated with each video.

All lecture slides as one file (PDF - 5.7MB)

LEC #	LECTURE NOTES
1	Probability Models and Axioms (PDF)
2	Conditioning and Bayes’ Rule (PDF)
3	Independence (PDF)
4	Counting (PDF)
5	Discrete Random Variables; Probability Mass Functions; Expectations (PDF)
6	Discrete Random Variable Examples; Joint PMFs (PDF)
7	Multiple Discrete Random Variables: Expectations, Conditioning, Independence (PDF)
8	Continuous Random Variables (PDF)
9	Multiple Continuous Random Variables (PDF)
10	Continuous Bayes’ Rule; Derived Distributions (PDF)
11	Derived Distributions; Convolution; Covariance and Correlation (PDF)
12	Iterated Expectations; Sum of a Random Number of Random Variables (PDF)
13	Bernoulli Process (PDF)
14	Poisson Process - I (PDF)
15	Poisson Process - II (PDF)
16	Markov Chains - I (PDF)
17	Markov Chains - II (PDF)
18	Markov Chains - III (PDF)
19	Weak Law of Large Numbers (PDF)
20	Central Limit Theorem (PDF)
21	Bayesian Statistical Inference - I (PDF)
22	Bayesian Statistical Inference - II (PDF)
23	Classical Statistical Inference - I (PDF)
24	Classical Inference - II (PDF)
25	Classical Inference - III; Course Overview (PDF)