Bayesian Data Analysis Global South (GSU) 2023
You are free to use these assignments in self study and other courses (CC-BY-NC 4.0), but please do not publish complete answers online.
If you are a student on this course, you are allowed to discuss assignments with your friends, but it is not allowed to copy solutions directly from other students, from the internet, or from large language models. You can copy, e.g., plotting code from the course demos, but really try to solve the actual assignment problems with your own code and explanations. Do not share your answers publicly.
|Block||Readings||Lectures||Assignment||Assignment due date|
|1. Introduction||BDA3 Chapter 1||Computational probabilistic modeling,
Introduction to uncertainty and modelling,
Introduction to the course contents
|Assignment 1 and Rubric questions||26 February|
|2. Basics of Bayesian inference||BDA3 Chapter 1,
BDA3 Chapter 2
Extra explanations 2,
|Assignment 2 and Rubric questions||6 March|
|3. Multidimensional posterior||BDA3 Chapter 3||Lecture 3||Assignment 3 and Rubric questions||13 March|
|4. Monte Carlo||BDA3 Chapter 10||Lecture 4.1,
|Assignment 4 and Rubric questions||20 March|
|5. Markov chain Monte Carlo||BDA3 Chapter 11||Lecture 5.1,
|Assignment 5 and Rubric questions||27 March|
|6. Stan, HMC, PPL||BDA3 Chapter 12 + extra material on Stan||Lecture 6.1,
|Assignment 6 and Rubric questions||3 April|
|7. Hierarchical models and exchangeability||BDA3 Chapter 5||Lecture 7.1,
|Assignment 7 and Rubric questions||17 April|
|8. Model checking & cross-validation||BDA3 Chapter 6, BDA3 Chapter 7, Visualization in Bayesian workflow, Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC||Lecture 8.1,
|Start project work|
|9. Model comparison and selection||BDA3 Chapter 7 (not 7.2 and 7.3),
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
|Assignment 8 and Rubric questions||1 May|
|10. Decision analysis||BDA3 Chapter 9||Lecture 10.1||Assignment 9 and Rubric questions||8 May|
|11. Normal approximation, frequency properties||BDA3 Chapter 4||Lecture 11.1,
|12. Extended topics||Optional: BDA3 Chapter 8,
BDA3 Chapter 14-18,
BDA3 Chapter 21
|Project work||15 May|
|13. Project feedback||Project feedback||22 May|
There are 9 weekly assignments. Assignments are linked from the schedule and can also be found from git repo assignments folder. The deadline days for the assignments are given in the course schedule.
Report all results in a single, anonymous *.pdf -file and submit it in peergrade. Submitting empty or almost empty pdf files is not allowed. Include also any source code to the report (either in appendix or embedded in the answer). By anonymity it is meant that the report should not contain your name or email. In addition to the correctness of the answers, the overall quality and clearness of the report is also evaluated.
The assignments are mostly solved using computer (R or Python). Related demos for each assignment are available in the course web pages (links in Materials section).
Overall recommendation for many students is to include more text explanations as writing that text is active way of learning, and also it’s easier to see if you have understood the concepts correctly. If you make minimal reports, you will also get minimal feedback and you learn less. The main purpose of the course is to learn, and learning is more effective if you do it in active and interactive way.
Register to peergrade with your email. The class code needed for registration will be posted to all registered students before the course starts.
Peergrading is called giving feedback in peergrade.io. The peergrading is guided by the rubric questions which are visible in peergrade and in the schedule. Giving feedback is good term as it is not meant to be just grading wrong vs. correct answers. Please provide also feedback on the presentation. Part of the course is practicing making data analysis reports. By providing feedback on the report presentation other students can learn what they can improve or what they already did well. You should be able to provide constructive or positive feedback for all non-empty reports, even if there is nothing to say about the technical correctness of the answers. If you all provide more feedback, you will all learn more about good data analysis reports. Be kind.
Points are given from both submitting an assignment and giving feedback. Submission performance gives 75% and feedback performance 25% of the total score. Only those students who returned a given assignment are allowed to give feedback to the answers submitted by others.
After peer grading is over for an assignment, each submission receives a submission score. This score is between 0% and 100% depending on how well the peers evaluated the submission. This is mostly about the technical correctness and small part about the presentation.
The numerical score for each criteria is normalised to be between 0% and 100%, where 0% is given to the worst, and 100% to the best possible answer. As an example, in a Scale-question with 5 levels, the different answers will count 0%, 25%, 50%, 75%, 100%.
The total submission score is calculated for a submission by averaging the scores from each of the peers. The score from an individual peer evaluating a submission is obtained by averaging over the criteria in the feedback rubric.
Within each assignment, there are two sections Basic requirements and Overall quality of the report. These segments are weighted so that they each provide 7.5% of that assignment’s total score. The remaining 85% is distributed evenly to the rest of the sections. Within each section, the points are distributed evenly among each question.
If a student has flagged some of their received evaluations and a TA has accepted the flag and overruled the original evaluation, then TA’s new evaluation is used for computing the score instead. If a teacher overrules a specific evaluation by one student, that answer is taken to be the correct one for that part of that submission. This means that the evaluations by other students on that part are disregarded in the final score of the submission.
There are 9 assignment rounds in total. Each round has different weighting when computing the total score. The assignment instructions list max points for each assignment as 3, 3, 9, 6, 6, 6, 6, 6, 3. These translate (approximately) to weights 6%, 6%, 19%, 12%, 12%, 12%, 12%, 12%, 6%.
The feedback score depends on the reactions, such that a student’s feedback score on an assignment is an average of the reactions received. If no reactions have been given to the student’s reviews, no feedback score is computed and it will simply show as “-”.
When you provide feedback, you can provide constructive and positive feedback n how to improve presentation or tell what is already very good. By providing that kind of feedback you are more likely to receive higher feedback score.
The following are reasons to react positively to feedback:
Be kind when reacting to to the feedback. If you didn’t like the feedback, think first a moment, and then re-consider whether it was just the wording that got you angry and maybe there is something useful in the feedback.
Note that the baseline feedback reaction is “Somewhat useful. Could be more elaborate.” which corresponds to 0.5 points or as Peergrade shows it to 50%. It might make you feel better if you consider that the middle option is 100%, as then you have already provided useful feedback which is the goal, and then the higher categories “Very useful” and “Extremely useful” would be 150% and 200% and you can consider receiving bonus points.
Remember to be polite when peergrading, giving feedback, and reacting to feedback. Do not spend a lot of time fighting over the grading of one question from one student. If you don’t agree you can submit a flag or in extreme case contact the TAs. Also, keep in mind that in most of the cases, which we’ve seen, the students have been fighting for points which have less than a 1/1000 effect on the final score. Long fight over that is not worth the time and energy. If you get feedback which makes you angry, breath and wait a moment before unleashing your anger back. We ask you to honor the system and be polite to your peers.
You can get help for the assignments by asking in the course chat from other students or in TA sessions by asking TAs.
As this course instance is based on volunteer TAs we can’t guarantee how many TA session we have each week. TA sessions will be announced during the course.
During the TA session you can get help in the following forms:
We will use the channel #queue in the course chat to coordinate everything. We announce there when the TA session starts. Then you can write your help request there, describing in sufficient detail exactly what is the problem with which you would need help (see below).
Once a TA is free and your question is the first request in the queue, a TA will mark it with a check mark reaction. Then the TA will contact you and help with your problem. Finally, once the problem is solved, the TA who helped you will delete your request from the queue.
Please do not send direct messages to TAs without going through the above protocol. If you have follow-up questions later, please put a new request to the queue.
Your help request should contain a concise summary of exactly what kind of help you would need. Ideally, after reading the help request, a TA should be able to already have an answer for you.
Try to describe what is the problem, what you have tried, what you already know, and exactly what is the relevant part of the code. Please highlight the important parts. Here are some fictional examples of good help requests:
Chat: I am not able to get the correct solution to assignment 2 part c) by using the test data. According to the assignment pdf, the correct solution is 0.4511238, but I am getting the answer 0.3771946. The code for my function is:
EXAMPLE CODE 1
I thought that I have an error in …, so I tried changing … to …, but then I got a different wrong answer, 0.4285443. I think the error may be on line 3 in the code, but I do not know how to fix that.
Zoom: I am trying to install the R package … on my personal laptop and I am getting the following error:
My operating system is …, I have version … of R installed and I am using RStudio. I tried googling the error but was not able to solve the issue. Zoom meeting link: https://aalto.zoom.us/j/XXX
TA session instructions above have been copied from Programming Parallel Computers by Jukka Suomela with CC-BY-4.0 license.