Computational Probability, Spring 2024


This course is all about variation, uncertainty, and randomness. Students will learn the vocabulary of uncertainty and the mathematical and computational tools to understand and describe it.


Thomas Stewart
Elson Building, 400 Brandon Ave, Room 156
Github: thomasgstewart

Teaching assistants

Ethan Nelson
Graduate student in Data Science
Github: eanelson01

Instruction & Office hours

Format of the class: In-class time will be a combination of lectures, group assignments, live coding, and student presentations. Please note: Circumstances may require the face-to-face portion of the class to be online.

Time: MWF, 10 - 10:50am, Dell 1 Room 105

Instructor Office Hours: MW, 11am, Dell 1 Commons (The instructor will leave if there are no questions after 15 minutes.)

TA Office Hours: Thursdays, 1pm, Dell 1 Commons


The following textbooks are freely available online via the UVA library.

Understanding uncertainty by Dennis V. Lindley

Understanding Probability, 3rd edition
by Henk Tijms

Introduction to Probability: Models and Applications
by N. Balakrishnan, Markos V. Koutras, Konstadinos G. Politis

The following textbooks may also be helpful.

Probability and Statistics for Data Science
by Norman Matloff

Introduction to Probability Models
by Sheldon M. Ross


The course will be taught using R.

Big ideas & Learning Outcomes

The following are the four ideas that I hope will persist with students after the minutia of the Poisson distribution has faded from memory. Expand each section to see the associated learning outcomes and topics.

Probability is a framework for organizing beliefs; it is not a statement of what your beliefs should be. | Learning outcomes | Topics | |:------|:---| | compare and contrast different definitions of probability, illustrating differences with simple examples |