stat_rethinking_2022
pymc-resources
| stat_rethinking_2022 | pymc-resources | |
|---|---|---|
| 13 | 6 | |
| 4,107 | 1,961 | |
| - | 0.6% | |
| 1.8 | 3.3 | |
| over 2 years ago | 6 months ago | |
| R | Jupyter Notebook | |
| - | MIT License |
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stat_rethinking_2022
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Is there another way to determine the effect of the features other than the inbuilt features importance and SHAP values? [Research] [Discussion]
I would recommend the lectures and book of Statistics Rethinking: https://github.com/rmcelreath/stat_rethinking_2022
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[Q] How is multilevel modelling different from a simple interaction/ moderation term?
Not a direct answer to your question, but I'd highly recommend Richard McElreath's lectures and book for learning multilevel modelling.
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Resources for learning Bayesian stats
Statistical Rethinking is an awesome Bayesian introductory course for people that already know some statistical modeling (i.e. GLM, HLM, ...) from the frequentist side.
- Boss wants me to model a process and tweak the parameters to minimize the response variable. How can I do that? e.g. number of customers waiting in a bank.
- Short Course on Statistics for Lab Scientists?
- Minimální znalost statistiky pro junior Data Scientist / Engineer pozici (ve finančnim sektoru)?
- Need a data set that I can do linear regression on but also apply hierarchical modelling via Bayesian methods.
- Statistical Rethinking (2022 Edition)
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How to be a biz/tech Anthropologist
Either way, try to pick up some computational and especially statistical expertise on the side. If you can't find coursework for it at your uni, I highly recommend McElreath's Statistical Rethinking. He recently started his lecture series for this year, with resources openly available: https://github.com/rmcelreath/stat_rethinking_2022
pymc-resources
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Statistical Rethinking (2024 Edition)
https://github.com/pymc-devs/pymc-resources/tree/main/Rethin...
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Bayesian Analysis with Python
As it happens, there's a PyMC implementation of the 1st and 2nd editions of Statistical Rethinking here:
https://github.com/pymc-devs/pymc-resources
(I think the author of the book discussed above, Osvaldo Martin, is the primary or sole contributor for the Rethinking implementations, in fact -- he had a full implementation in his own repo [here](https://github.com/aloctavodia/Statistical-Rethinking-with-P...) before deprecating it in favor of the above-linked one.)
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Predicting the distribution of a variable rather than a point estimate
That course/book has been translated to Python (using PyMC3 for the modeling, but you could also use the Stan examples and run them from Python using CmdStanPy).
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Statistical Rethinking (2022 Edition)
Prof. McElreath has been adding two new videos every week.
Also, for anyone who prefers to use the pythons for the coding, I recommend the PyMC3 notebooks https://github.com/pymc-devs/resources/tree/master/Rethinkin... There is also a discussion forum related to this repo here https://gitter.im/Statistical-Rethinking-with-Python-and-PyM...
- Statistical rethinking, but with examples in python?
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Stan is a state-of-the-art platform for statistical modeling
The Statistical Rethinking book uses R.
For people wanting Python, Jupyter notebooks with Python code examples are here:
* https://github.com/pymc-devs/resources/tree/master/Rethinkin...
What are some alternatives?
stat_rethinking_2020 - Statistical Rethinking Course Winter 2020/2021
neural-tangents - Fast and Easy Infinite Neural Networks in Python
botorch - Bayesian optimization in PyTorch
skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
interpretable-ml-book - Book about interpretable machine learning
indaba-pracs-2022 - Notebooks for the Practicals at the Deep Learning Indaba 2022.
stat_rethinking_2023 - Statistical Rethinking Course for Jan-Mar 2023
cookiecutter-pystan
Statistical-Rethinking-with-P
Statistical-Rethinking-with-Python-and-PyMC3 - Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath
BayesianEcosystems_IAP - Notes and code for Bayesian ecosystem modeling IAP course
numpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.