MultiBUGS
PyMC
| MultiBUGS | PyMC | |
|---|---|---|
| 1 | 3 | |
| 31 | 8,186 | |
| - | 0.8% | |
| 0.0 | 9.5 | |
| almost 3 years ago | about 1 hour ago | |
| Shell | Python | |
| GNU Lesser General Public License v3.0 only | GNU General Public License v3.0 or later |
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.
MultiBUGS
-
Project Oberon Emulator in JavaScript and Java
Oberon is awesome. I typically use a variant of it called Component Pascal. It is a very esoteric, and quite unique language.
My usage of it is in WinBUGS/OpenBUGS/MultiBUGS [1], for Markov chain Monte Carlo statistical analysis. It's really cool and works amazingly well for systems of differential equations too.
The version I recommend using is MultiBUGS [2]. I would avoid installing it in Windows, though!
[1] https://www.mrc-bsu.cam.ac.uk/software/bugs/
[2] https://github.com/MultiBUGS/MultiBUGS
PyMC
- PYMC Release: v5.0.0
-
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
-
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?
rstan - RStan, the R interface to Stan
statsmodels - Statsmodels: statistical modeling and econometrics in Python
paramonte - ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
Dask - Parallel computing with task scheduling
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.
numpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Numba - NumPy aware dynamic Python compiler using LLVM
security - Collection of CVEs from Sick Codes, or collaborations on https://sick.codes security research & advisories.
SymPy - A computer algebra system written in pure Python
pyro - Deep universal probabilistic programming with Python and PyTorch
SciPy - SciPy library main repository