Build a probabilistic model for medical diagnosis that estimates the probability of a condition given observed test results.
Combine a neural network with a statistical prior in a single Pyro model to build a Bayesian deep learning system.
Run automatic variational inference on a custom statistical model to fit it to observed data using Pyro's SVI interface.
| pyro-ppl/pyro | togethercomputer/openchatkit | octoprint/octoprint | |
|---|---|---|---|
| Stars | 9,001 | 8,989 | 8,985 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | hard | moderate |
| Complexity | 4/5 | 4/5 | 3/5 |
| Audience | researcher | researcher | general |
Figures from each repo's GitHub metadata at analysis time.
Requires PyTorch installed first, GPU recommended for larger models but not required.
Pyro is a Python library for a style of programming called probabilistic programming. In regular programming, code produces a single definite output for a given input. In probabilistic programming, you write code that describes probability distributions: models of how uncertain quantities relate to each other. The system then figures out, given some observed data, which settings of the unknown quantities best explain what was seen. This matters for problems where you need to reason under uncertainty. A medical diagnostic model might ask: given these test results, what is the probability the patient has condition X? A financial model might ask: given past price movements, what distribution of future returns should we expect? Pyro gives researchers and engineers a way to write those kinds of models in Python and then run automatic statistical inference on them. Pyro is built on PyTorch, a widely used library for deep learning (the technology behind many modern AI systems). This means Pyro models can combine traditional statistical components with neural networks in the same model. The library is designed to scale to large datasets and to be composable: complex models are built by combining simpler pieces. The project was originally developed at Uber's AI research team and is now maintained by community contributors including a team at the Broad Institute, a biomedical research organization. In 2019, it joined the Linux Foundation as an official open source project. It is installed as a Python package via pip and comes with a collection of example models and tutorials covering common use cases. The README includes a citation for the original research paper published in the Journal of Machine Learning Research for those who want to reference the work formally.
A Python library for probabilistic programming that lets you write statistical models describing uncertainty and then automatically infer the best parameters from observed data.
Mainly Python. The stack also includes Python, PyTorch, pip.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
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