MA540 Introduction to Probability Theory

Instructor: Nikolay S. Strigul

Office Hours: by appointment

Lectures:Pierce 116, Monday 6:15 pm -8:45 pm

Assignments, Exams, Quizzes: There will be regular homework assignments, midterm and final exams. Every class will start with a closed book quiz consisting of definitions and theorems.

Grading:

- Quizzes: 20 %
- Homework assignments: 20 %

- Midterm: 20 %

- Final: 40 %

Course programCourse program (PDF)

General comments:MA540 is a graduate-level introduction in probability theory. Compared to undergraduate-level courses in probability and statistics, all theorems will be rigorously proven—however we will not use the measure theory as in more advanced courses. Topics will include sample space and events, combinatorial methods for computing probabilities, random variables, expectations, limit theorems, and stochastic processes. Theoretical problems will be illustrated by computer simulations using Mathematica software. We will follow William Feller's classic "An Introduction to Probability Theory and Its Applications". Homework assignments will also have both theoretical and computational components. For the computational part we will use Jenny A. Baglivo's "Mathematica Laboratories for Mathematical Statistics: Emphasizing Simulation and Computer Intensive Methods".

Textbooks

1) An Introduction to Probability Theory and Its Applications by William Feller, V. 1, ISBN: 0471257087

2) Mathematica Laboratories for Mathematical Statistics: Emphasizing Simulation and Computer Intensive Methods by Jenny A. Baglivo, ISBN: 0898715660

Course program:Lecture 1.- The Nature of Probability Theory.

1. "Statistical probability"

2. The empirical background.

3. The sample space, events.

4. Relations among events.

5. Introduction to Mathematica.

Homework 1.pdf

Lecture 2.- Elements of Combinatorial Analysis.

6. Subpopulations and partitions.

7. The hypergeometric distribution.

8. Binomial coefficients.

9. Stirling's formula.

Homework 2.pdf Quiz 1.pdf

Lecture 3.- Fluctuations of Coin Tossing and Random Walks.

10. The reflection principle.

11. Random walks.

12. Computer simulations of random walks.

Homework 3.pdf Quiz 2.pdf

Lecture 4.- Combination of events. Stochastic independence.

13. Union of events.

14. The realization of m among N events.

15. Conditional probability.

16. Stochastic independence.

17. Application to genetics.

Homework 4.pdf Quiz 3.pdf

Sep 26. Lecture 5.- The Binomial distribution.

18. Bernoulli trials.

19. The Binomial distribution.

20. The central term and the tails.

21. The Law of Large Numbers.

Homework 5.pdf Quiz 4.pdf

Lecture 6.- The Poisson distribution.

22. The Poisson approximation to the Binomial distrinution.

23. The Poisson distribution.

24. Applications of the Poisson distribution.

25. The Negative Binomial distribution.

Homework 6.pdf Quiz 5.pdf

Lecture 7.- The Normal Approximation to the Binomial Distribution.

26. The normal distribution.

27. The DeMoivre-Laplace Limit Theorem.

28. Relation to the Poisson Approximation.

Homework 7.pdf Quiz 6.pdf

Lecture 8.- Random Variables.

29. Random variables.

30. Expectations.

31. Variance.

32. Covariance.

33. Chebyshev's Inequality.

Homework 8.pdf Quiz 7.pdf

Lecture 9.- Laws of Large Numbers.

34. Identically distributed variables.

35. Proof of the law of large numbers.

36. The theory of 'fair' games.

37. Variable distributions.

38. The strong law of large numbers.

Homework 9.pdf Quiz 8.pdf

Lecture 10.- Generating functions.

39. Convolutions.

40. Waiting times in Bernoulli trials.

41. Partial fraction expansions.

42. Bivariate generating functions.

Homework 10.pdf Quiz 9.pdf

Lecture 11.- Random walks.

43. The classical ruin problem.

44. Application of generating functions.

45. Connections with diffusion process.

46. Random walks in plane and space.

Homework 11.pdf Quiz 10.pdf

Lecture 12.- Markov Chains I.

47. Definitions and illustrative examples.

48. Transition probabilities.

49. Classification of states.

50. Irreducible chains. Decomposition.

Homework 12.pdf Quiz 11.pdf

Lecture 13.- Markov Chains II.

51. Transient chains.

52. Periodic chains.

53. The general Markov process.

Homework 13.pdf Quiz 12.pdf

Lecture 14.- Time-dependent stochastic processes.

54. The Chapman-Kolmogorov identity.

55. The Poisson process.

56. The backward equations.

Quiz 13.pdf

Final exam.

back