Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
LEARNING OUTCOMES
After the course the student will be familiar with main instances how algebraic methods can be used in statistics.
Credits: 5
Schedule: 09.09.2020 - 04.12.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Kaie Kubjas
Teacher in charge (applies in this implementation): Kaie Kubjas
Contact information for the course (valid 17.08.2020-21.12.2112):
The communication for this course takes place in Zulip. There is a separate channel for each problem set and assignment. Please be active asking your questions!
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
The contents of this course include the following topics:
- Algebra primer
- Statistics primer
- Conditional independence
- Likelihood inference
- Fisher's exact test and Markov bases
- Graphical models
- Hidden variable models
- Identifiability
Time permitting further topics will be considered.
Applies in this implementation:
In this course, we will cover selected topics from “Algebraic Statistics” textbook by Seth Sullivant. The topics include:
- Algebra primer
- Probability primer
- Conditional independence
- Statistics primer
- Exponential families
- Likelihood inference
- Fisher’s exact test
- Graphical models
- Hidden variables
Time permitting, further topics will be covered.
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
Teaching methods: lectures and exercises
Assessment methods: participation and homework
Applies in this implementation:
This course is graded pass/fail. For passing the course, one has to attend at least 10 lectures, receive at least 70% of maximal possible points on homework sets and complete all additional assignments. Additional reading assignments and the group project are not graded by points.
Workload
Valid 01.08.2020-31.07.2022:
Contact hours 48h, self-study ca 96h
Applies in this implementation:
There will be 12 lectures and 12 exercise sessions. The rest of workload comes from self-study.
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
Main textbook:
Seth Sullivant "Algebraic Statistics"
Further reading:
Drton, Sturmfels, Sulivant "Lectures on Algebraic Statistics"
Pacher, Sturmfels "Algebraic Statistics for Computational Biology"
Bocci, Chiantini "An Introduction to Algebraic Statistics with Tensors"
Applies in this implementation:
The textbook for this course is “Algebraic Statistics” by Sullivant.
Additional textbooks on Algebraic Statistics are:
- “Lectures on Algebraic Statistics” by Drton, Sturmfels, and Sullivant
- “An Introduction to Algebraic Statistics with Tensors” by Bocci and Chiantini
- “Algebraic Statistics for Computational Biology” by Pachter and Sturmfels
Prerequisites
Valid 01.08.2020-31.07.2022:
MS-A050X First course in probability and statistics; MS-C134X Lineaarialgebra / Linear algebra; MS-C1081 Abstract algebra (recommended)
FURTHER INFORMATION
- Teacher: Kubjas Kaie
- Teacher: Kuznetsova Olga