https://github.com/tallamjr/bap-numpyro
Bayesian Analysis in Python (2nd ed.) with Numpyro
https://github.com/tallamjr/bap-numpyro
bayesian-data-analysis bayesian-inference laplace-approximation markov-chain-monte-carlo numpyro variational-inference
Last synced: about 1 year ago
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Bayesian Analysis in Python (2nd ed.) with Numpyro
- Host: GitHub
- URL: https://github.com/tallamjr/bap-numpyro
- Owner: tallamjr
- License: mit
- Created: 2021-09-15T11:37:47.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-11-03T13:31:03.000Z (over 4 years ago)
- Last Synced: 2025-02-10T02:38:12.074Z (about 1 year ago)
- Topics: bayesian-data-analysis, bayesian-inference, laplace-approximation, markov-chain-monte-carlo, numpyro, variational-inference
- Language: Jupyter Notebook
- Homepage: https://tallamjr.github.io/bap-numpyro/
- Size: 62.3 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Bayesian Analysis in Python (2nd ed.) with Numpyro
After discovering the fantastic
[`rethinking-numpyro`](https://fehiepsi.github.io/rethinking-numpyro/) project by
[@fehiepsi](https://github.com/fehiepsi), I was inspired to try and do something like that myself.
Primarily as a learning activity, this is my attempt at porting
[@aloctavodia](https://github.com/aloctavodia)'s "Bayesian Analysis in Python" [example PyMC3
code](https://github.com/aloctavodia/BAP) to [NumPyro](https://github.com/pyro-ppl/numpyro)
🚧 **WIP** 🚧
_**NOTE**_ : This is still a work-in-progress project.
I am still very new to `numpyro` myself, and therefore welcome comments and suggestions about how
best to write idiomatic code and translate things etc. My plan is to translate as much as possible
using _only_ `jax`, `arviz`, `numpyro` and `seaborn`. There are aspects where I could not figure
out how to do it, maybe you can help?!
#### TODOs:
- [ ] Fix Chapters 5, 6, 7
- [ ] Complete port of exercises 3 and 4
- [ ] Complete exercises for chapters 5, 6, 7 & 8
- [ ] Remove unused imports from each notebook
## Acknowledgements
Many many thanks to [Du Phan](https://github.com/fehiepsi) for the foundations of which I have built
this from and [Osvaldo Martin](https://github.com/aloctavodia) for the amazing book: Bayesian Data
Analysis in Python