stat_rethinking_2022
Statistical Rethinking course winter 2022 (by rmcelreath)
botorch
Bayesian optimization in PyTorch (by pytorch)
| stat_rethinking_2022 | botorch | |
|---|---|---|
| 13 | 5 | |
| 4,107 | 3,114 | |
| - | 0.8% | |
| 1.8 | 9.5 | |
| over 2 years ago | 1 day ago | |
| R | Jupyter Notebook | |
| - | MIT License |
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_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.
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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
botorch
Posts with mentions or reviews of botorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-06.
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botorch VS SMT - a user suggested alternative
2 projects | 6 Dec 2023
- BoTorch – Bayesian Optimization in PyTorch
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[D] Uncertainty estimation with calibration set (with MC Dropout)
The true answer for this is modelling the problem bayesian in the first place using, for example, https://botorch.org/ and https://gpytorch.ai/.
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Bayesian Optimization Book
Yes, I'm using a binary outcome, since that's what I get from playing a game. To get probabilities I'd have to play a lot of games with the same settings/features/point and take the mean, but it seems that defeats the point of Bayesian optimization finding the best point to evaluate for each iteration.
The SPSA method seems to work quite well with binary outcomes. This is what I was trying to beat. Unfortunately I was never able to converge faster than SPSA (or even close to that) even increasing the number of samples.
I got some feedback form the botorch team back then: https://github.com/pytorch/botorch/issues/347#:~:text=thomas...
What are some alternatives?
When comparing stat_rethinking_2022 and botorch you can also consider the following projects:
stat_rethinking_2020 - Statistical Rethinking Course Winter 2020/2021
Ax - Adaptive Experimentation Platform
interpretable-ml-book - Book about interpretable machine learning
smt - Surrogate Modeling Toolbox
stat_rethinking_2023 - Statistical Rethinking Course for Jan-Mar 2023
noisy-bayesian-optimization - Bayesian Optimization for very Noisy functions
pymc-resources - PyMC educational resources
optimas - Optimization at scale, powered by libEnsemble