pgmpy
pyhf
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| pgmpy | pyhf | |
|---|---|---|
| 2 | 1 | |
| 2,617 | 271 | |
| 1.4% | 1.1% | |
| 8.0 | 8.8 | |
| 6 days ago | 3 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
pgmpy
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Use data from tables generated in python console,
No need to post the help, here is the DiscreteFactor class https://github.com/pgmpy/pgmpy/blob/eb65f40d2b32bf2ad971181333bb9ed7aefde907/pgmpy/factors/discrete/DiscreteFactor.py
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[D] Python toolboxes for probabilistic graphical model inference
I do know of a few promising toolboxes such as pgmpy, pymc3, and pyro, but have not used either of them (for this purpose) and am at a bit of a loss picking one to start with.
pyhf
What are some alternatives?
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
MetPy - MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.
statsmodels - Statsmodels: statistical modeling and econometrics in Python
uproot5 - ROOT I/O in pure Python and NumPy.
scikit-learn - scikit-learn: machine learning in Python
hist - Histogramming for analysis powered by boost-histogram
CausalPy - A Python package for causal inference in quasi-experimental settings
generalized-additive-models - Generalized Additive Models in Python.
rustworkx - A high performance Python graph library implemented in Rust.
uproot3 - ROOT I/O in pure Python and NumPy.
Lottery-Simulation - This program can simulate a number of drawings in the Lottery (6 out of 49). The guesses and the draws are chosen randomly and the user can choose how many right guesses there should be (0-6). Then the program will run through the simulation as many times as it takes to get the exact number of correct guesses the user chose. The user can also choose how many times this should be repeated (the higher the number, the more accurate the result will be). Then the program will automatically calculate the average number of tries it took to get the chosen number of correct guesses and tell the user the chance of getting this certain number of correct guesses.
dodiscover - [Experimental] Global causal discovery algorithms