stat_rethinking_2020
Statistical Rethinking Course Winter 2020/2021 (by rmcelreath)
stan
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details. (by stan-dev)
| stat_rethinking_2020 | stan | |
|---|---|---|
| 8 | 44 | |
| 651 | 2,521 | |
| - | 0.5% | |
| 2.6 | 9.4 | |
| about 3 years ago | 12 days ago | |
| R | C++ | |
| - | BSD 3-clause "New" or "Revised" 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_2020
Posts with mentions or reviews of stat_rethinking_2020.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-07.
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[Q] Book on Bayesian statistics?
Bayesian rethinking is quite a good book and has been translated to Python.
- [E] Statistical Rethinking 2022 by Richard McElreath
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I have a small sample size time series with potentially lagged predictor values which are also time series. What could be potential methods to analyse these data?
If you want a full course on Bayesian Multilevel models, there's the excellent "statistical rethinking": lectures/content here and code here
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How to incorporate Bayes Inference into inplay betting model using R?
If you're unfamiliar with Bayesian analysis, I recommend reading Richard McElreath's Statistical Rethinking. It has associated R exercises and a lectures (found here)
- Any good video series for learning Bayesian stats?
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Quantitative Methods Course
A wonderful introductory course from a Bayesian point of view: https://github.com/rmcelreath/stat_rethinking_2020
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Any resource suggestion for 6420 Bayesian Statistics?
https://github.com/rmcelreath/stat_rethinking_2020 includes slides and videos.
stan
Posts with mentions or reviews of stan.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-14.
- Stan: Statistical modeling and high-performance statistical computation
-
Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
Alternatives: stan and edward
- How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have?
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Rstan Package in ATPA
remove.packages(c("StanHeaders", "rstan")) install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
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[Q] Is there a method for adding random effects to an interval censored time to event model?
My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions.
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HELP Conjugate Priors in Bayesian Regression in SPSS
Here is a good breakdown of recommendations from Andrew Gelman.
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Demand Planning
For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes.
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What do actual ML engineers think of ChatGPT?
I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but learning from their design and emulating what makes them successful.
- ChatGPT4 writes Stan code so I don’t have to
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How to get started learning modern AI?
oh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing
[1] https://mc-stan.org/
What are some alternatives?
When comparing stat_rethinking_2020 and stan you can also consider the following projects:
stat_rethinking_2022 - Statistical Rethinking course winter 2022
PyMC - Bayesian Modeling and Probabilistic Programming in Python
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
rstan - RStan, the R interface to Stan
Elo-MMR - Skill estimation systems for multiplayer competitions
probability - Probabilistic reasoning and statistical analysis in TensorFlow
pyro - Deep universal probabilistic programming with Python and PyTorch
MultiBUGS - Multi-core BUGS for fast Bayesian inference of large hierarchical models
fastbaps - A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data
rnim - A bridge between R and Nim