CAS 781: Advanced Topics in Bayesian Networks - Summer 2018



Calendar Description

Bayesian Networks: A review of Bayesian versus frequentist statistics, the role of uncertainty and prior knowledge, and the reason Bayesian statistics provides a better model for “learning”. How to model causality with a labelled directed graph. Making inferences from a BN. Using expert knowledge to create a BN. Learning the structure of BNs from data. How the structure of the graph determines the computational complexity of these processes, and determines the best available algorithms. Applications.

Course Objective

In this course you will learn how to model inference problems using Bayesian Networks (and to a lesser extent, Markov Networks), how to estimate the complexity of inference and of learning.


Probabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir Friedman, MIT Press, 2009.


Christopher Anand, ETB 112, x21397. anandc (circled a) (name of university) (country). Make an appointment by email.


Thursdays, 15:00-19:00, ETB 119, 2018/06/18 - 2018/08/03


10 - Contribution to Class Discussion

10 - Presentation of Assigned Material (e.g, statisical paradoxes)

30 - Presentation of Example (e.g., from a journal paper)

50 - Project


You are expected to exhibit honesty and use ethical behaviour in all aspects of the learning process. Academic credentials you earn are rooted in principles of honesty and academic integrity. Academic dishonesty is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage. This behaviour can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: "Grade of F assigned for academic dishonesty"), and/or suspension or expulsion from the university. 

It is your responsibility to understand what constitutes academic dishonesty. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at 

The following illustrates only three forms of academic dishonesty: 1. Plagiarism, e.g. the submission of work that is not one's own or for which other credit has been obtained. 2. Improper collaboration in group work. 3. Copying or using unauthorized aids in tests and examinations.

If in doubt, ask the instructor how this applies to your work.


In this course we reserve the right to use a web-based service ( to reveal plagiarism. Students will be expected to submit their work electronically to and in hard copy so that it can be checked for academic dishonesty. Students who do not wish to submit their work to must still submit a copy to the instructor. No penalty will be assigned to a student who does not submit work to All submitted work is subject to normal verification that standards of academic integrity have been upheld (e.g., on-line search, etc.). To see the Policy, please go to

Personal Information

In this course we will be using subversion, email and other on-line discussion fora. Students should be aware that, when they access the electronic components of this course, private information such as first and last names, user names for the McMaster e-mail accounts, and program affiliation may become apparent to all other students in the same course. The available information is dependent on the technology used. Continuation in this course will be deemed consent to this disclosure. If you have any questions or concerns about such disclosure please discuss this with the course instructor.

Possible Changes

The instructor and university reserve the right to modify elements of the course during the term. The university may change the dates and deadlines for any or all courses in extreme circumstances. If either type of modification becomes necessary, reasonable notice and communication with the students will be given with explanation and the opportunity to comment on changes. It is the responsibility of the student to check their McMaster email and course websites weekly during the term and to note any changes.