MAT 135B (Stochastic Processes) Winter 2023, A01 (CRN 45196)
      MWF 1:10-2:00PM, 146 Olson
http://www.math.ucdavis.edu/~gravner/MAT135B/

INSTRUCTOR:
TA:

PREREQUISITES: An excellent knowledge of calculus and basic linear algebra (i.e., courses MAT 21ABC and MAT 22A or similar) and an ability to understand and devise a mathematical argument (i.e., MAT 108 or similar). You also need to have working knowledge of the material from MAT 135A or STA 131A, covered by Chapter 1-8 of the lecture notes available on the materials page. You are responsible for satisfying the prerequisites!

TEXTBOOK: My lecture notes are available free of charge at the materials page. Chapters 9-18 will be covered. For additional reading, you can use the book Introduction to Probability Models, by Sheldon Ross (Elsevier). The latest edition is 12th, but earlier (or later) editions are fine. Our main focus will be on sections 3.1-3.6, 4.1-4.8, 5.1-5.3.

GRADE:

Course grade will be based on the following:

I will follow this grading curve:

  • 0-40%: F
  • 41-50%: D
  • 51-65%: C
  • 66-80%: B
  • 81-100%: A

ADDITIONAL POLICIES:

I will make everything as predictable as I can, which I think is necessary more than ever. In particular, course policies will not change (either for the class in general or for particular students) due to world events, unless otherwise ordered by the university administration.

Thursday meeting (5:10-6pm at 1 Wellman) is a discussion session, lead by the TA, and mostly devoted to homework and further elaboration on lecture material. Attendance of discussion sessions is mandatory.

Please bear in mind that talking, texting, etc. disrupt the lectures. Use of computers, cellphones, recorders, or any other electronic devices during lectures is prohibited, except for the purpose of taking notes.

If you have any problem at all that requires special accomodation, please let me know well in advance!

Various class materials, including exams (with solutions) from the last time I taught this course are available on the materials page. However, material presented in the lectures will be the basis for the exams.

Homework will be assigned every Friday, and will be due on the following Friday, by the end of the lecture. Late homework will not be accepted in any circumstance. Full solutions to each homework will be posted on the materials page at least 24 hours before the homework is due. You may use the solutions to check or complete your work, but you may not copy the solutions. Also, there is always a possibility that a solution contains a mistake, so please check all posted solutions carefully (and this applies to all solutions, not just the homework ones).

Use of books, notes, calculators, or anything else but pencil and paper, will not be allowed on any exam.

There will be no make-up exams. A missed exam counts as 0 points. The grade I (Incomplete) will not be given in any circumstances. If you miss much of the coursework because of illness or other emergency, please petition for the Retroactive Drop.

Solutions for the midterms will be posted on the materials page.

SOME USEFUL LINKS:

  • Computer computations and simulations are a very useful tool in probability. I will use MATLAB for this purpose, but you may instead use its free alternative Octave, which suffices for our purposes (and scripts are mutually compatible).
  • Book by C. M. Grinstead and J. L. Snell, Introduction to Probability.
  • Random Services developed by Kyle Siegrist from University of Alabama at Huntsville.
  • Introduction to probability, statistics, and random processes developed by Hossein Pishro-Nik from University of Massachusetts at Amherst.
  • How to Gamble if You Must by Kyle Siegrist.
  • Random Walks and Electric Networks by P. G. Doyle, J. L. Snell.