tensorflow/probability
Probabilistic reasoning and statistical analysis in TensorFlow
This project helps data scientists, statisticians, and machine learning engineers analyze data using advanced probabilistic methods. You input your raw data and specify a statistical model (like a mixed-effects model or a Bayesian neural network), and it outputs insights, predictions, or classifications with quantified uncertainty. It's designed for those who need to understand not just 'what' but 'how certain' about their data.
4,417 stars. Actively maintained with 1 commit in the last 30 days.
Use this if you need to build sophisticated statistical models, quantify uncertainty in your predictions, or perform advanced data analysis with deep learning techniques.
Not ideal if you primarily need simple descriptive statistics or basic predictive models without a focus on probabilistic inference or uncertainty quantification.
Stars
4,417
Forks
1,120
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 04, 2026
Commits (30d)
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/tensorflow/probability"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks....
probml/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
google/edward2
A simple probabilistic programming language.
astro-informatics/harmonic
Machine learning assisted marginal likelihood (Bayesian evidence) estimation for Bayesian model selection