Probabilistic Systems Analysis and Applied Probability

Calendar

LEC #	TOPICS	KEY DATES
1	Probability models and axioms	Problem set 1 out
2	Conditioning and Bayes’ rule
3	Independence	Problem set 1 due Problem set 2 out
4	Counting
5	Discrete random variables; probability mass functions; expectations	Problem set 2 due Problem set 3 out
6	Discrete random variable examples; joint PMFs
7	Multiple discrete random variables: expectations, conditioning, independence	Problem set 3 due Problem set 4 out
8	Continuous random variables
9	Multiple continuous random variables	Problem set 4 due Problem set 5 out
	Quiz 1 (covers lectures 1-7)
10	Continuous Bayes rule; derived distributions
11	Derived distributions; convolution; covariance and correlation	Problem set 5 due Problem set 6 out
12	Iterated expectations; sum of a random number of random variables
13	Bernoulli process
14	Poisson process - I	Problem set 6 due Problem set 7 out
	Quiz 2 (covers up to lecture 12)
15	Poisson process - II
16	Markov chains - I	Problem set 7 due Problem set 8 out
17	Markov chains - II
18	Markov chains - III	Problem set 8 due Problem set 9 out
19	Weak law of large numbers
20	Central limit theorem	Problem set 9 due Problem set 10 out
21	Bayesian statistical inference - I
22	Bayesian statistical inference - II
23	Classical statistical inference - I	Problem set 10 due Problem set 11 out (not to be handed in)
24	Classical inference - II
25	Classical inference - III; course overview