STA510 — Bayesian Methods

Interactive study reference for the course modules and a workflow tracker for the individual project

Welcome

This app follows the six modules of the STA510 course notes, from what a probability is to a full Bayesian workflow for generalised linear mixed models (GLMM), and ends with a step-by-step guide for your individual project. The core idea is the same everywhere: prior × likelihood → posterior, and then everything you want (estimates, intervals, predictions, decisions) comes from the posterior or from draws of it.

Foundations

Seminars 1–2

Computing and checking

Seminars 3–4

GLMM and workflow

Seminar 5, lectures, project

How to study with it

  1. Read a module, Part A first. Part A is concepts with quizzes and needs no code. Every section has a status control under its heading: Not reviewed / Reviewed / Confident. Click as you go.
  2. Then Part B. Small R exercises run in your browser (Run Code); the RStudio tasks use brms on your own computer. Each task simulates its own data and has a hidden solution, tested with brms 2.23.
  3. Work through the project on the project page: a to-do list in ten steps, each with a status tracker (Not started / In progress / Done) and checkboxes for the tasks within it.
  4. Check the dashboard for what is left, and look up terms in the glossary or with search (top right).

What the badges mean

Badge Meaning
🎯 Syllabus outcome The section directly covers one of the intended learning outcomes in the STA510 syllabus. The outcomes are official; which section covers which outcome was decided for this app.
🧩 Needed for your project AI-inferred: a skill you will use in the individual GLMM project. Not stated by the teacher.
NoteThe course is examined by seminars and a project, not a written exam

Seminar participation (at least four of five, with a self-reflection before each) and an individual project with a written report and an oral presentation. So the app does not predict exam questions. It aims at understanding the modules well enough to discuss them, and at doing the project well.

Code blocks marked Run Code are interactive: R runs in your browser, no installation needed. The first run on a page takes a few seconds while R loads.