Student course projects: schedule and formats

You present the project as both For the in-class presentation you may (and most students do) use a laptop and slides. It saves time if you email the presentation to me and then use my laptop. Or you may do a chalk talk, in which case it's good to make a handout of data, graphics etc, then e-mail to me 1 day in advance.

The write-up should be in usual "term paper" form -- that is, grammatical English sentences not Powerpoint slides. If you're analyzing data, then graphics are helpful. I'm looking for clear exposition rather than a literary masterpiece. Email the write-up to me as PDF. I will post a few of the best write-ups on the course web site -- see this page for old ones.

Schedule of talks


Thursday November 16 Tuesday November 21 Tuesday November 28 Thursday November 30 Tuesday December 5

STAT 157: Seminar on Topics in Probability and Statistics

Probability and the Real World (Fall 2017)

Instructor: David Aldous

GSI None.

NEW 9/26. Summary comments on your 7-minute talks; for the record, the schedule of talks is here.

Class time: Tuesday Thursday 2.00 - 3.30 in room 330 Evans.

Office Hours: Wednesdays 9.30 - 11.30 in 351 Evans.

Prerequisite: Upper division probability (STAT 134 or equivalent). The course emphasizes student participation and initiative while offering students the opportunity to pursue intellectual curiosity in directions of their individual choice. It is limited to 36 students.

Courses in mathematical probability teach you to do certain mathematical calculations, but these are often far removed from broader questions about the the role of randomness in the "real world" of science or of human affairs. In contrast, this junior/senior seminar course seeks to engage such questions in two ways.

1. In lectures I will treat about 20 different topics, one each lecture, chosen to illustrate the diversity of contexts where probability arises. Some idea of this diversity can be gleaned from my list of 100 contexts where we perceive chance.

2. A recurrent theme is to adopt a classical science paradigm: can we use probability theory to make predictions about the real world which can be verified or falsified by experiment or observation?

The requirements for students are (see the link below for more info).

There are no other homeworks or exams. There is no course textbook, but to get into the spirit of the course it's helpful to read one of the ``popular science" books on the book list.

The Pre-Quiz

To be admitted to this course, you must first do the pre-quiz on this page.

UPDATE 8/29. THE CLASS IS FULL: no more students except those I have already contacted.

More information

Read here for more about administration and deadlines.

This page is a guide to online resources which may be helpful in choosing projects. It's intended for online browsing.

Class-by-class schedule

Material below will be posted after each lecture.

The links below go to slides of the lectures, which will be posted after the lecture. The link here goes to extended write-ups of about half of the lectures. The link here goes to links, books and papers mentioned in lectures -- implicit suggestions for further reading.

Th 8/24: Lecture 1: Everyday perception of chance.

Tu 8/29: Lecture 2: The Kelly criterion for favorable games: stock market investing for individuals.
Th 8/31: Lecture 3: Sports rating models.

Tu 9/5: Lecture 4: Risk to Individuals: Perception and Reality.
Th 9/7: Lecture 5: Predicting the future: Geopolitics etc.

Tu 9/12: Lecture 6: Coincidences, near misses and one-in-a-million chances.
Th 9/14 Lecture 7: Game theory.

Tu 9/19 : Student talks
Th 9/21: Student talks

Tu 9/26: Student talks
Th 9/28: Lecture 8: Luck .

Tu 10/3: Lecture 9: Prediction markets, fair games and martingales.
Th 10/5 Lecture 10: Psychology of probability: predictable irrationality.

Tu 10/10: Lecture 11: Coding and entropy.
Th 10/12 Lecture 12: Science fiction meets science.

Tu 10/17: Lecture 13: Physical Randomness and the Local Uniformity Principle.
Th 10/19 Lecture 14: Size-biasing, regression effect and dust-to-dust phenomena.

Tu 10/24: Lecture 15: A glimpse at probability research: spatial networks on random points. Selected slides from this 2010 talk and this 2014 talk.
Th 10/26 Lecture 16: Branching processes, tipping points and phase transitions.

Tu 10/31: Lecture 17: Toy models in population genetics: some mathematical aspects of evolution.
Th 11/2 Lecture 18: Toy models of human interaction: use and abuse.

Tu 11/7: Lecture 19: Miscellany 1.
Th 11/9 Lecture 20: Miscellany 2.

Tu 11/14: NO CLASS
Th 11/16 Student talks

Tu 11/21: Student talks
Th 11/23 No class

Tu 11/28: Student talks
Th 11/30 Student talks

Tu 12/5: Student talks

Wordle: My "probability in the real world" course