# probml/pmtk3

Probabilistic Modeling Toolkit for Matlab/Octave.

Repository: https://github.com/probml/pmtk3
Canonical: https://ross.abutalabs.com/products/pmtk3
Language: HTML
License: MIT
License Family: permissive
Last push: 2021-06-23T15:08:28+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4625, "days_push": 1897, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1569, forks 784 (observed 2026-08-28T04:05:05.283578+00:00)

## What it is
PMTK3 is a Matlab/Octave toolkit of probabilistic machine learning models and algorithms, created to accompany Kevin Murphy's textbook 'Machine Learning: a Probabilistic Perspective'. It provides reference implementations of regression, SVMs, Gaussian processes, graphical models, latent variable models, and Bayesian inference methods.

## Use cases
- implement probabilistic machine learning models in matlab
- learn algorithms from the machine learning a probabilistic perspective textbook
- run bayesian inference examples like MCMC and variational bayes
- explore reference implementations of EM, HMMs, and graphical models
- reproduce textbook figures and exercises in octave

## When to choose
- you are studying Murphy's textbook and want matching Matlab/Octave code
- you need readable reference implementations of classic probabilistic ML algorithms
- you work in Matlab or Octave and want a broad ML toolbox

## When to avoid
- you need maintained software with bug fixes or new features
- you prefer Python - use the successor pyprobml instead
- you need production-grade performance or modern deep learning support

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, data-science, math
- domain: machine-learning, data-science, education
- platform: -
- tags: matlab, octave, bayesian-statistics, graphical-models, textbook-companion, unmaintained, algorithms

## Member repositories
- probml/pmtk3 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:05.283578+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 2026-08-30T03:58:53.991095+00:00, confidence not recorded.
  - readme: https://github.com/probml/pmtk3 (fetched 2026-08-28T04:05:05.283578+00:00, sha e4672b5a3c84)
- Data as of 2026-08-30T08:39:29.467469+00:00.