SDST4610 Bayesian learning (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota
Course Co-ordinator Prof E C H Fong, SCDS (Department of Statistics and Actuarial Science) < chefong@hku.hk >
Teachers Involved (Prof E C H Fong,Statistics & Actuarial Science)
Course Objectives This course will provide a comprehensive introduction to the Bayesian framework for statistical inference. Students will learn how to apply advanced simulation techniques for posterior computation, which also have wider applications within statistics. This course is particularly suitable for students who intend to pursue further studies or a career in research.
Course Contents & Topics This course covers the fundamental Bayesian framework, including prior elicitation, posterior inference and model selection. For posterior computation, Monte Carlo methods such as importance sampling and Markov chain Monte Carlo will be introduced. Methods for approximate inference such as variational Bayes will also be covered. Advanced Bayesian modeling with nonparametric Bayes will then be explored, with applications in machine learning.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 execute the Bayesian pipeline, including prior elicitation, posterior summarization and model evaluation
CLO 2 simulate from complex probability distributions using advanced Monte Carlo methods such as Markov chain Monte Carlo
CLO 3 apply posterior approximation techniques such as Laplace's approximation and variational Bayes
CLO 4 develop nonparametric Bayesian models and apply them in machine learning
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3600 or SDST3602 or SDST3603 or SDST3902 or SDST3903 or SDST3907
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2024 Major in Decision Analytics ( Disciplinary Elective )
2024 Major in Risk Management ( Disciplinary Elective )
2024 Major in Statistics ( Disciplinary Elective )
2024 Minor in Risk Management ( Disciplinary Elective )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2023 Major in Decision Analytics ( Disciplinary Elective )
2023 Major in Risk Management ( Disciplinary Elective )
2023 Major in Statistics ( Disciplinary Elective )
2023 Minor in Risk Management ( Disciplinary Elective )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2022 Major in Decision Analytics ( Disciplinary Elective )
2022 Major in Risk Management ( Disciplinary Elective )
2022 Major in Statistics ( Disciplinary Elective )
2022 Minor in Risk Management ( Disciplinary Elective )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2021 Major in Decision Analytics ( Disciplinary Elective )
2021 Major in Risk Management ( Disciplinary Elective )
2021 Major in Statistics ( Disciplinary Elective )
2021 Minor in Risk Management ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2024 Major in Decision Analytics < PLO 1,2,3,6 >
2024 Major in Risk Management < PLO 1,2,3,4,6 >
2024 Major in Statistics < PLO 1,2,3,4,6 >
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2023 Major in Decision Analytics < PLO 1,2,3,6 >
2023 Major in Risk Management < PLO 1,2,3,4,6 >
2023 Major in Statistics < PLO 1,2,3,4,6 >
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2022 Major in Decision Analytics < PLO 1,2,3,6 >
2022 Major in Risk Management < PLO 1,2,3,4,6 >
2022 Major in Statistics < PLO 1,2,3,4,6 >
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2021 Major in Decision Analytics < PLO 1,2,3,6 >
2021 Major in Risk Management < PLO 1,2,3,4,6 >
2021 Major in Statistics < PLO 1,2,3,4,6 >
Offer in 2025 - 2026 Y        1st sem    Examination Dec     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and class test(s)) 50.0 1,2,3,4
Examination One 2-hour written examination 50.0 1,2,3,4
Required/recommended reading
and online materials		 1. Bayesian Data Analysis, by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin (CRC Press, 2013). http://www.stat.columbia.edu/~gelman/book/
Course Website http://moodle.hku.hk
Additional Course Information