| 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) |
