Bayesian Data Analysis Global South (GSU) 2023
Summary
- Max 300 students with priority for global south and other
underrepresented groups (GSU).
- From 18th February (first assignment deadline 26th February, last
day of the course in the end of May)
- All the material (textbook, videos, assignments, extra reading
material) are freely available (see below) so you can also self-study at
your own pace.
- The course is free (no cost) and possible to organize with help of
volunteer TAs.
- This BDA course instance is aimed to support learning with peer
support. By following the videos and doing assignments at the same time
with others, you can discuss the material in assignments in the course
slack, there is peer-grading platform to get feedback about your
assignment solutions, and voluntary TAs help answering questions. As
everything is volunteer based we can’t guarantee quick responses, but at
least you will get something more than when studying only by
yourself.
- This course is not the easiest Bayesian course available in
internet, but it can be your first Bayesian course if your mathematical
and programming skills are sufficient. Compared to some other free
online courses, this course has big emphasis in the computational
methods, and thus goes deeper in many parts. See the prerequisites
below. For easier material to start with see the end of Prerequisites
section below.
- You will not get a formal certificate for passing the course from
Aalto University.
- The communication happens in the course slack, please don’t email
the lecturer or TAs. The slack link has been emailed to the accepted
students.
All the course material is available in a git repo and via
these pages for easier navigation. All the material can be used in other
courses. Text and videos licensed under CC-BY-NC 4.0. Code licensed
under BSD-3.
Registration
You can apply for the course by filling a registration form. The
registration form asks several pre-requisites questions, and filling the
form takes some time. We will email whether you have been accepted to
the course at latest 17th February (Finland time). If you are not
accepted for the course, you can still self-study all the material.
TA Registration
We have some volunteer TAs already, but a few more would be great.
All TAs will get a personal certificate from the lecturer Aki Vehtari if
they actively participate helping students, answering questions, and
possibly organize some TA sessions. We assume to have enough TAs that
no-one needs to take part every week of the course and you can drop out
if other obligations require so. The lecturer and two head TAs will
support TAs.
To register as volunteer TA fill in your information (email, country,
prerequisites check, a brief comment on justification why you think you
can be a TA) in TA
registration form.
Prerequisites
- Basic terms of probability theory
- Some algebra and calculus
- Basic visualisation techniques (R or Python)
This course has been designed so that there is strong emphasis in
computational aspects of Bayesian data analysis and using the latest
computational tools.
If you find BDA3 too difficult to start with, I recommend
- For regression models, their connection to statistical testing and
causal analysis see Gelman, Hill and
Vehtari, “Regression and Other Stories”.
- Richard McElreath’s Statistical
Rethinking, 2nd ed book is easier than BDA3 and the 2nd ed is
excellent, but doesn’t go as deep in computational methods as this BDA
course. Richard’s lecture videos of Statistical
Rethinking: A Bayesian Course Using R and Stan are highly
recommended even if you are following BDA3.
- Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu Bayes Rules! An Introduction to
Applied Bayesian Modeling book is excellent and easier than BDA3,
but doesn’t go as deep in computational methods as this BDA course.
- For background prerequisites some students have found chapters 2, 4
and 5 in Kruschke,
“Doing Bayesian Data Analysis” useful.
Communication channels
- The primary communication channel is the course slack. The slack
link has been emailed to the accepted students.
- Don’t ask via email or direct messages. By asking via common
channels in the course chat, more eyes will see your question, it will
get answered faster and it’s likely that other students benefit from the
answer.
- In the chat system, there are separate channels for each assignment
and the project.
- Channel #general can be used for any kind of
general discussions and questions related to the course.
- All important announcements will be posted to
#announcements (no discussion on this channel).
- Any kind of feedback is welcome on channel
#feedback.
- We have also channels #r, #python,
and #stan for questions that are not specific to
assignments or the project.
- The lecturer and teaching assistants have names with “(staff)” or
“(TA)” in the end of their names.
- In this course instance, most TAs are volunteers from different time
zones, but there may be possibility for live “TA sessions” with TAs
depending on the volunteers
- If you find errors in material, post in #feedback
channel or submit an
issue in github.
- Peergrade alerts: If you are worried that you forget the deadlines,
you can set Peergrade to send you email when assignment opens for
submission, 24 hours before assignment close for submission, assignment
is open for reviewing, 24 hours before an assignment closes for
reviewing if you haven’t started yet, someone likes my feedback (once a
day). Click your name -> User Settings to choose which alerts you
want.
Assessment
Assignments (67%) and a project work with presentation (33%).
Minimum of 50% of points must be obtained from both the assignments and
project work. But as in this course there is no formal certificate, the
assignment scores are just for your own self-evaluation. The biggest
benefit from the course is the support and feedback from other students
and volunteer TAs.
We use peergrade.io for providing
peer feedback for the assignments and the project work. See more
information at Assignments.
Schedule 2023
The course consists of 12 blocks from February to May 2023. The
blocks don’t match exactly specific weeks. For example, it’s good start
reading the material for the next block while making the assignment for
one block. There are 9 assignments and a project work with presentation,
and thus the assignments are not in one-to-one correspondence with the
blocks. The schedule below lists the blocks and how they connect to the
topics, book chapters and assignments.
Schedule overview
Here is an overview of the schedule. Scroll down the page to see
detailed instructions for each block. Remember that blocks are
overlapping so that when you are working on assignment for one block,
you should start watching videos and reading text for the next
block.
Note: All assignment deadlines are on Monday 15:59
UTC+0
|
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 |
Lecture
2.1, Lecture
2.2, Optional: Extra
explanations 2, Summary
2.1, Summary
2.2 |
Assignment 2 and Rubric
questions |
|
6 March |
|
3. Multidimensional posterior |
BDA3 Chapter 3 |
Lecture
3 |
Assignment 3, Rubric questions |
|
13 March |
|
4. Monte Carlo |
BDA3 Chapter 10 |
Lecture
4.1, Lecture
4.2 |
Assignment 4, Rubric questions |
|
20 March |
|
5. Markov chain Monte Carlo |
BDA3 Chapter 11 |
Lecture
5.1, Lecture
5.2 |
Assignment 5, Rubric questions |
|
27 March |
|
6. Stan, HMC, PPL |
BDA3 Chapter 12 + extra material on Stan |
Lecture
6.1, Lecture
6.2 |
Assignment 6, Rubric questions |
|
3 April |
|
7. Hierarchical models and exchangeability |
BDA3 Chapter 5 |
Lecture
7.1, Lecture
7.2 |
Assignment 7, 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, Lecture
8.2 |
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 |
Lecture
9.1, Optional: Lecture
9.2, Lecture
9.3 |
Assignment 8, Rubric questions |
|
24 April |
|
10. Decision analysis |
BDA3 Chapter 9 |
Lecture
10.1 |
Assignment 9, Rubric questions |
|
8 May |
|
11. Normal approximation, frequency properties |
BDA3 Chapter 4 |
Lecture
11.1, Lecture
11.2 |
|
|
|
|
12. Extended topics |
Optional: BDA3 Chapter 8, BDA3 Chapter 14-18, BDA3 Chapter
21 |
Optional: Lecture
12.1, Lecture
12.2 |
Project work |
|
15 May |
|
13. Project feedback |
|
|
Project feedback |
|
22 May |
1) Course introduction, BDA 3 Ch 1, prerequisites assignment
Course practicalities, material, assignments, project work,
peergrading, TA sessions, prerequisites, chat, etc.
- Login to the course chat (link will be emailed to the registered
students and volunteer TAs)
- Sign in to Peergrade with the
class code shared in email
- Watch videos
- There are no R/Python demos for Chapter 1
- Make and submit Assignment 1.
Deadline Sunday 26 February 23:59 UTC+0
- This assignment checks that you have sufficient prerequisite skills
(basic probability calculus, and R or Python)
- Rubric questions used in peergrading are included above
- General information about
assignments
- Optional: Make BDA3 exercises 1.1-1.4, 1.6-1.8 (model
solutions available for 1.1-1.6)
- Start reading Chapters 1+2, see instructions below
2) BDA3 Ch 1+2, basics of Bayesian inference
BDA3 Chapters 1+2, basics of Bayesian inference, observation model,
likelihood, posterior and binomial model, predictive distribution and
benefit of integration, priors and prior information, and one parameter
normal model.
- Read BDA3 Chapter 2
- Watch videos Lecture
2.1 and Lecture
2.2 on basics of Bayesian inference, observation model, likelihood,
posterior and binomial model, predictive distribution and benefit of
integration, priors and prior information, and one parameter normal
model. BDA3 Ch 1+2.
- Optional summary videos:
- Read the additional
comments for Chapter 2
- Check R demos or Python demos for Chapter 2
- Make and submit Assignment 2.
Deadline Monday 6 March 15:59 UTC+0
- Rubric questions used in peergrading are included above
- Review Assignment 1 done by your peers before 15:59 UTC+0 4
March
- Reflect on your feedback
- Optional: Make BDA3 exercises 2.1-2.5, 2.8, 2.9, 2.14, 2.17, 2.22
(model
solutions available for 2.1-2.5, 2.7-2.13, 2.16, 2.17, 2.20, and
2.14 is in course slides)
- Start reading Chapter 3, see instructions below
3) BDA3 Ch 3, multidimensional posterior
Multiparameter models, joint, marginal and conditional distribution,
normal model, bioassay example, grid sampling and grid evaluation. BDA3
Ch 3.
- Read BDA3 Chapter 3
- Watch Lecture
3 on multiparameter models, joint, marginal and conditional
distribution, normal model, bioassay example, grid sampling and grid
evaluation. BDA3 Ch 3.
- Read the additional
comments for Chapter 3
- Check R demos or Python demos for Chapter 3
- Make and submit Assignment
3. Deadline Monday 13 March 15:59 UTC+0
- Optional: Make BDA3 exercises 3.2, 3.3, 3.9 (model
solutions available for 3.1-3.3, 3.5, 3.9, 3.10)
- Start reading Chapter 10, see instructions below
4) BDA3 Ch 10, Monte Carlo
Numerical issues, Monte Carlo, how many simulation draws are needed,
how many digits to report, direct simulation, curse of dimensionality,
rejection sampling, and importance sampling. BDA3 Ch 10.
5) BDA3 Ch 11, Markov chain Monte Carlo
Markov chain Monte Carlo, Gibbs sampling, Metropolis algorithm,
warm-up, convergence diagnostics, R-hat, and effective sample size. BDA3
Ch 11.
6) BDA3 Ch 12 + Stan, HMC, PPL, Stan
HMC, NUTS, dynamic HMC and HMC specific convergence diagnostics,
probabilistic programming and Stan. BDA3 Ch 12 + extra material
7) BDA3 Ch 5, hierarchical models
Hierarchical models and exchangeability. BDA3 Ch 5.
9) BDA3 Ch 7, extra material, model comparison and selection
PSIS-LOO, K-fold-CV, model comparison and selection. Extra lecture on
variable selection with projection predictive variable selection.
10) BDA3 Ch 9, decision analysis
Decision analysis. BDA3 Ch 9.
- Read Chapter 9
- Watch Lecture
10.1 on decision analysis. BDA3 Ch 9.
- Project presentation info will be updated soon.
- Make and submit Assignment
9. Deadline Monday 8 May 15:59 UTC+0
- Start reading Chapter 4, see instructions below.
13) Project evaluation
- Project report deadline 7 May 15:59 UTC+0 (submit to peergrade).
- Review project reports done by your peers before
22 May 15:59 UTC+0, and reflect on your feedback
R and Python
We strongly recommend using R in the course as there are more
packages for Stan and statistical analysis in R. If you are already
fluent in Python, but not in R, then using Python may be easier, but it
can still be more useful to learn also R. Unless you are already
experienced and have figured out your preferred way to work with R, we
recommend
See FAQ for frequently asked questions about R
problems in this course. The demo codes provide
useful starting points for all the assignments.
- For learning R programming basics
- For learning basic and advanced plotting using R