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401-4619-67L 4 Credits DR , MSC D-ITET , D-MATH , D-INFK

Advanced Topics in Computational Statistics

Does not take place this semester.

VVZ CR n/a

Last Updated: 2026-02-05 15:35:16

Abstract

This lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling.

Objective

Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.

Content

The main focus will be on graphical models in various forms: Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models

General Information

Language: English
Levels: DR , MSC

Examination

Type: session examination
Mode: written 120 minutes
Aids: None

Students must have taken the exam in Winter 2018 or in Summer 2018. Be aware that no exam will be offered afterwards until the course will be read again.

Course Components

Type	Title	Time & Place	Hours
lecture	Advanced Topics in Computational Statistics Does not take place this semester.	No time listed	2 h weekly

Offered In

Computational Science and Engineering Master
- Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
Mathematics Master
- Electives (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 15 of the required 28 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
  - Electives: Applied Mathematics and Further Application-Oriented Fields (¬)
    - Selection: Probability Theory, Statistics
Electrical Engineering and Information Technology Master
- Master Studies (Programme Regulations 2018)
  - Signal Processing and Machine Learning (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
    - Specialisation Courses (These specialisation courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the MSc EEIT.)
- Master Studies (Programme Regulations 2008)
  - Major Courses (A total of 42 CP must be achieved during the Master Programme. The individual study plan is subject to the tutor's approval.)
    - Signal Processing and Machine Learning
      - Recommended Subjects
Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
- Master Studies (Programme Regulations 2020)
  - Subject Specific Electives
- Master Studies (Programme Regulations 2014)
  - Specialization Areas and Electives
    - Statistical and Mathematical Courses
Doctoral Department of Mathematics (More Information at: The list of courses (together with the allocated credit points) eligible for doctoral students is published each semester in the newsletter of the ZGSM. WARNING: Do not mistake ECTS credits for credit points for doctoral studies!)
- Graduate School (Official website of the Zurich Graduate School in Mathematics:)
Data Science Master
- Core Courses
  - Core Electives