blackjax
pgmpy
| blackjax | pgmpy | |
|---|---|---|
| 1 | 2 | |
| 727 | 2,627 | |
| 3.3% | 1.1% | |
| 8.2 | 8.0 | |
| 3 days ago | 22 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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blackjax
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AutoBNN: Probabilistic Time Series Forecasting
What are the other four frameworks?
> For one, who wants to do stuff in tensorflow anymore let alone tensorflow-probability.
AutoBNN is a JAX library and has nothing to do technically with TF Probability. It was developed by the TF Probability team.
> DL community prefers pytorch and stats community prefers Stan.
It looks like the JAX ecosystem for stats is growing: NumPyro is based on JAX, PyMC has a JAX backend, https://github.com/blackjax-devs/blackjax has effective samplers, there is https://github.com/jax-ml/bayeux, and now AutoBNN.
> This one seems theoretically more interesting than some others but practically less useful.
Are there other factors why you think AutoBNN is not practically useful, apart from being based on the wrong foundation (which was a mistaken belief of yours)?
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.
What are some alternatives?
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
statsmodels - Statsmodels: statistical modeling and econometrics in Python
scikit-learn - scikit-learn: machine learning in Python
CausalPy - A Python package for causal inference in quasi-experimental settings
rustworkx - A high performance Python graph library implemented in Rust.
pyhf - pure-Python HistFactory implementation with tensors and autodiff
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
generalized-additive-models - Generalized Additive Models in Python.
auton-survival - Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
HumesGuillotine - Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.
dowhy - DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.