Contents

pymc 5.27.1

0

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTen

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor

Stars: 9480, Watchers: 9480, Forks: 2208, Open Issues: 461

The pymc-devs/pymc repo was created 16 years ago and the last code push was Yesterday.
The project is extremely popular with a mindblowing 9480 github stars!

How to Install pymc

You can install pymc using pip

pip install pymc

or add it to a project with poetry

poetry add pymc

Package Details

Author
None
License
Apache License, Version 2.0
Homepage
http://github.com/pymc-devs/pymc
PyPi:
https://pypi.org/project/pymc/
GitHub Repo:
https://github.com/pymc-devs/pymc

Classifiers

  • Scientific/Engineering
  • Scientific/Engineering/Mathematics
No  pymc  pypi packages just yet.

Errors

A list of common pymc errors.

Code Examples

Here are some pymc code examples and snippets.

GitHub Issues

The pymc package has 461 open issues on GitHub

  • Derive Categorical from Argmin/Argmax
  • Fix multi-output IfElse logp for non-lazy backends
  • Improve links and references in dims module notebook
  • Fix model_to_graphviz for Flat variables
  • logprob for non-overlapping switch accepts equivalent spellings
  • Add rewrites with RV reparametrization tricks
  • ENH: support non-overlapping switch logp transforms with non-zero thresholds (x ? k)
  • ENH: general framework for non-overlapping switch logp transforms (monotone branches)
  • ENH: support non-overlapping switch logp with scaling on both branches
  • ENH: extending equivalent non-overlapping switch logp forms
  • Implements more Dims Distributions
  • SMC Multiprocessing and Progress Bar Refactor
  • Feature/transform aware bounds checking
  • Replace legacy seeded_test fixture with NumPy Generator in tests
  • Respect value dtype in partial_observed_rv_logprob

See more issues on GitHub

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pyflux is a Python library designed for time series analysis. It includes modern time series models, including ARIMA, GARCH, and state space models. With a flexible set of inference options, pyflux enables a probabilistic approach to time series modeling. Though still in alpha, it's an invaluable tool for statisticians and data scientists working with time series data.