arviz
PyMC
| arviz | PyMC | |
|---|---|---|
| 3 | 3 | |
| 1,529 | 8,155 | |
| 1.2% | 0.6% | |
| 7.9 | 9.5 | |
| 10 days ago | 7 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
arviz
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Matplotlib for sabermetric analysis
pymc3 is the standard Python library for Bayesian statistics, and used ArviZ for plotting, built on top of matplotlib
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What is Probabilistic Programming?
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
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Hacktoberfest: 69 Beginner-Friendly Projects You Can Contribute To
https://github.com/arviz-devs/arviz Exploratory analysis of Bayesian models with Python
PyMC
- PYMC Release: v5.0.0
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An Astronomer's Introduction to NumPyro
I believe the pymc versions were resolved into developing version 4 of pymc. Development at https://github.com/pymc-devs/pymc
It still depends on theano now evolved and renamed
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What is Probabilistic Programming?
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
What are some alternatives?
matplotlib - matplotlib: plotting with Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python
Babel (Formerly 6to5) - 🐠 Babel is a compiler for writing next generation JavaScript.
Dask - Parallel computing with task scheduling
seaborn - Statistical data visualization in Python
stan - Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
Numba - NumPy aware dynamic Python compiler using LLVM
probability - Probabilistic reasoning and statistical analysis in TensorFlow
SymPy - A computer algebra system written in pure Python
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pyro - Deep universal probabilistic programming with Python and PyTorch