Introduction to Probability for Computing

1. preface-and-contents
2. before-we-start-some-mathematical-basics
3. probability-on-events
4. common-discrete-random-variables
5. expectation
6. variance-higher-moments-and-random-sums
7. z-transforms
8. continuous-random-variables-single-distribution
9. continuous-random-variables-joint-distributions
10. normal-distribution
11. heavy-tails-the-distributions-of-computing
12. laplace-transforms
13. the-poisson-process
14. generating-random-variables-for-simulation
15. event-driven-simulation
16. estimators-for-mean-and-variance
17. classical-statistical-inference
18. bayesian-statistical-inference
19. tail-bounds
20. applications-of-tail-bounds-confidence-intervals-balls-and-bins
21. hashing-algorithms
22. las-vegas-randomized-algorithms
23. monte-carlo-randomized-algorithms
24. primality-testing
25. discrete-time-markov-chains-finite-state
26. ergodicity-for-finite-state-discrete-time-markov-chains
27. discrete-time-markov-chains-infinite-state
28. a-little-bit-of-queueing-theory