The course is in the third cycle and amounts to 5 credits. Course syllabus for NAMV005.

Apply to the course: at this link. The spots are limited. The application process will close on 24 Dec 2021 or as the spots will up, whichever comes first. You will receive the email with the confirmation if you are accepted.

Dates: 17 Jan - 07 Feb 2022

Teachers: Dmytro Perepolkin and Ullrika Sahlin

Meetings: The course is structured around four meetings: three virtual meetings (Zoom) and one in-person meeting in Lund (on 07 Feb 2022).

Materials: The course is primarily based on BR* (see References section), because it is free and contains a lot of examples, but we will also refer to some material in SR* and SG*. Slides and other study materials will be published on Canvas prior to lectures.

Software: R, Rstudio and Stan. See installation instruction here

Schedule:

Monday, 17 Jan 2022 (9-12)

Introduction to Bayesian inference and subjective probability

SR* (Chapters 1 and 2 )

BR* (Chapters 1 and 2)

SG* (Chapters 4, 5, 7)

This corresponds, roughtly, to Lectures 1-2 in Richard McElreath 2022 video lectures

Monday, 24 Jan 2022 (9-12)

Lecture Conjugate models with exercises

SR* (Chapter 4 )

BR* (Chapters 3 and 5)

SG* (Chapters 8 and 9)

Introduction to Modern Bayesian analysis with MCMC

This corresponds, roughly, to Lectures 3-4 in Richard McElreath 2022 video lectures

Monday, 31 Jan 2022 (9-12)

Lecture on Modern Bayesian analysis with MCMC with exercises

SR* (Chapter 9)

BR* (Chapters 6,7,8)

SG* (Chapters 12 -16)

Introduction to Hierarchical models

Monday, 07 Feb 2022 (hybrid meeting in Lund) (10-16)

Lecture on Hierarchical models with exercises

SR* (Chapters 10, 11 and 13 )

BR* (Chapters 9-11, 13-17)

SG* (Chapters 17 -19)

Lecture on Decision theory

1*,2*

Literature seminar

Literature and instructions are found here

References:

*BR - Alicia A. Johnson, Miles Ott, Mine Dogucu Bayes Rules https://www.bayesrulesbook.com/

*SR - Richard McElreath Statistical Rethinking https://xcelab.net/rm/statistical-rethinking/ - this book is available in electronic format for students at Lund University link (you may need to use VPN to access it, maximum of 60 pages to download or print)

*SG - Ben Lambert Student’s Guide to Bayesian Statistics https://ben-lambert.com/a-students-guide-to-bayesian-statistics/

*1 Justin Silverman Bayesian Decision Theory Made Ridiculously Simple, blogpost http://www.statsathome.com/2017/10/12/bayesian-decision-theory-made-ridiculously-simple/

*2 Stan User Guide Chapter 31 Decision Analysis https://mc-stan.org/docs/2_28/stan-users-guide/decision-analysis.html

We will also have a literature seminar about uncertainty and decision making - see Literature seminar