stat_rethinking_2023
Statistical Rethinking Course for Jan-Mar 2023 (by rmcelreath)
stat_rethinking_2022
Statistical Rethinking course winter 2022 (by rmcelreath)

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stat_rethinking_2023 | stat_rethinking_2022 | |
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7 | 13 | |
2,216 | 4,112 | |
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4.0 | 1.8 | |
about 1 year ago | almost 3 years ago | |
R | R | |
Creative Commons Zero v1.0 Universal | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
stat_rethinking_2023
Posts with mentions or reviews of stat_rethinking_2023.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-05.
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Hitting the Jackpot: The Birth of the Monte Carlo Method – LANL
I'm currently going through the Statistical Rethinking [0] class on Bayesian statistics, and it reminded me that Bayesian statistics' renaissance was basically thanks to Monte Carlo methods. Such methods can approximate posterior distributions that are often extremely difficult to calculate analytically.
[0] https://github.com/rmcelreath/stat_rethinking_2023
- Statistical Rethinking Course for Jan-Mar 2023
- Statistical rethinking 2023 course archive
- Statistical Rethinking for 2023
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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]
The 2023 version is currently in process: https://github.com/rmcelreath/stat_rethinking_2023
- Statistical Rethinking (2023 Edition)
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[E] FYI: Statistical Rethinking (2023) by rmcelreath
The course will be about causal inference and Bayesian data analysis. You can find the course materials on GitHub - rmcelreath/stat_rethinking_2023: Statistical Rethinking Course for Jan-Mar 2023.
stat_rethinking_2022
Posts with mentions or reviews of stat_rethinking_2022.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-05.
-
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
What are some alternatives?
When comparing stat_rethinking_2023 and stat_rethinking_2022 you can also consider the following projects:
botorch - Bayesian optimization in PyTorch
interpretable-ml-book - Book about interpretable machine learning
pymc-resources - PyMC educational resources
stat_rethinking_2020 - Statistical Rethinking Course Winter 2020/2021
noisy-bayesian-optimization - Bayesian Optimization for very Noisy functions

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Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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Nutrient - The #1 PDF SDK Library
Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free.
nutrient.io
featured