# avehtari/BDA_course_Aalto

Bayesian Data Analysis course at Aalto

Repository: https://github.com/avehtari/BDA_course_Aalto
Canonical: https://ross.abutalabs.com/products/bda_course_aalto
Homepage: https://avehtari.github.io/BDA_course_Aalto/
Language: TeX
License Family: other
Topics: bayes, bayesian, bayesian-data-analysis, bayesian-inference, bayesian-methods, bayesian-workflow
Last push: 2026-08-26T13:46:30+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 2917, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2273, forks 566 (observed 2026-08-28T04:06:33.240344+00:00)

## What it is
Complete course materials for the Bayesian Data Analysis course (CS-E5710) at Aalto University, taught by Aki Vehtari and based on the BDA3 textbook by Gelman et al. The repository contains lecture notes, slides, exercises, videos, and R/Python demos, all licensed for reuse in other courses.

## Use cases
- learn bayesian data analysis from a university course
- self-study bayesian inference and MCMC methods
- find exercises and demos for bayesian statistics
- reuse bayesian course material for teaching my own class
- learn computational bayesian methods with r and python
- supplement the BDA3 book with videos and assignments
- understand bayesian workflow for data analysis

## When to choose
- you want structured, university-grade material for learning Bayesian data analysis alongside the BDA3 textbook
- you are an instructor looking for freely reusable course content, slides, and exercises
- you prefer learning computational aspects of Bayesian statistics through hands-on R and Python demos
- you want up-to-date material that is refreshed each course term

## When to avoid
- you need a software library or tool for performing Bayesian inference - use Stan, PyMC, or similar instead
- you want a self-contained textbook rather than supplementary course material
- you need commercial-use rights - the text and videos are CC-BY-NC licensed
- you are looking for frequentist or general statistics education rather than Bayesian methods

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, math
- domain: data-science, education, mathematics, machine-learning, tutorials
- platform: cross-platform
- tags: bayesian-statistics, bayesian-inference, course-material, statistics, mcmc, stan, lecture-notes, r-demos, python-demos, tex

## Member repositories
- avehtari/BDA_course_Aalto (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:33.240344+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:41:41.532889+00:00, confidence not recorded.
  - readme: https://github.com/avehtari/BDA_course_Aalto (fetched 2026-08-28T04:06:33.240344+00:00, sha d1ed467b9385)
  - homepage: https://avehtari.github.io/BDA_course_Aalto/ (fetched 2026-08-29T10:22:02.898687+00:00, sha 92a8cdc126d1)
- Data as of 2026-08-30T08:39:29.467469+00:00.