STAT 4010 - Bayesian Learning

Assessment and Grading 

There are three main assessment components, plus a bonus component. 

• • •  a (out of 100) is the average score of approximately eight assignments with the lowest two scores dropped; 

    • m (out of 100) is the score of mid-term assessment; 

    • f (out of 100) is the score of final assessment; and

    • b (out of 2) is the bonus points, which will be given to students who actively participate in class.

The total score t (out of 100) is given by 

t = min{100, 0.3a + 0.2max(m,f ) + 0.5f + b}

If min(t, f ) < 30, the final letter grade will be handled on a case-by-case basis. Otherwise, your letter grade will be in the A range if t ≥ 85, at least in the B range if t ≥ 65, at least in the C range if t ≥ 55.

Important note: For the most updated information, please always refer to the course outline announced by the course instructor in Blackboard, which shall prevail the above information if there is any discrepancy.

In-class Materials

Click (S4010/2024Spring/L/inclassNote) and (S4010/2024Spring/L/recording) to download all in-class notes and recordings (if any). 

• Lecture 1 (11 Jan): Introduction to Bayesian Statistics, posterior calculation. [Sorry that the screen was not recorded this time]

• Lecture 2 (18 Jan): Examples of posterior calculation, introduction to non-informative prior. [Example 1.4 of the lecture note was updated.]

• Lecture 3 (25 Jan): Invariant priors, Jeffreys priors, violation of likelihood principle, conjugate priors and informative priors.

• Lecture 4 (1 Feb): Conjugate prior for exponential family, mixture distribution, commonly used Bayesian estimators, decision theory.

• Lecture 5 (8 Feb): Derivation of commonly used Bayes Estimators, admissibility (Mini-talk of luck)

• Lecture 6 (22 Feb): Admissibility, Minimaxity, proofs of Chapter 3. [Sorry that the sound was not recorded this time]

• Lecture 7 (29 Feb): 0-1 loss for testing, generalized 0-1 loss, Bayes factor, modified Bayes factor 

• Lecture 8 (14 Mar): Problems of Bayesian testing, region estimation, HPD credible region

• Lecture 9 (21 Mar): Asymptotic theory, De Finetti's theorem, classical and MC methods 

• Lecture 10 (28 Mar): Computational methods, Example 7.8

• Lecture 11 (11 Apr): Convergence diagnosis, Gibbs sampler, MH-within-Gibbs sampler

• Lecture 12 (18 Apr): Regression, BIC, spike-and-slab prior, LASSO

• Lecture 13 (22 Apr): Missing data, EM, MI, empirical Bayes 

Remark:  In-class notes and recordings (if any) will be uploaded within one week after the lecture. 

Quizzes 

Click (S4010/2024Spring/Q) to download all assignments.

• • Quiz 1: Posterior calculation 

  • Quiz 2: Prior specification 

  • Quiz 3: Bayes estimation

Final Project 

• • Start time: 3 May (Friday), 10:00 am  

  • End time: 6 May (Monday), 10:00 am  

  • Duration: 72 hours

  • Scope: Chapters 1-10

  • Instructions: The detailed instructions are stated on the first page of the real question paper. Some highlights are listed below: 

    • • Read the instructions carefully before doing the project. 

      • Complete the project by yourself. 

      • Consult and use any official course materials if you wish.

      • The project will be graded according to the criterion referencing scheme (see here).

  • Submission: 

    • • Compile your answers in a single ".pdf" file (i.e., not MS words, jpeg, zip, etc). 

      • Sign the Honor Code, and attach it as a cover of your submitted file. 

      • Name the document in the format S4010_F_sid_name.pdf., e.g., S4010_F_1155001234_ChanKinWai.pdf. 

      • Submit to Blackboard. You may submit your answers as many times as you wish, however, only the last submission will be graded.

      • All plots, numerical answers, simulation results, etc must be included in the written part. Graders will not run your submitted codes to check the answers.

  • Mock final projects

    • • Quick review exercise for each chapter. (Recording from 2021 Spring)

      • Mock 1: 2023 Spring F -- Most relevant

      • Mock 2: 2022 Spring F 

      • Mock 3: 2021 Spring F

      • Mock 4: 2019 Fall F

      • Mock 5: 2019 Fall F0

  • Real final project 

    • • Question paper: (S4010/2024Spring/F) -- The password will be sent to your CUHK email sharply at the project start time.