stan
autodiff
stan | autodiff | |
---|---|---|
44 | 6 | |
2,521 | 35 | |
0.5% | - | |
9.4 | 3.6 | |
13 days ago | 9 months ago | |
C++ | C | |
BSD 3-clause "New" or "Revised" License | MIT License |
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stan
- Stan: Statistical modeling and high-performance statistical computation
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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/
autodiff
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A Command-line Program of Neural Networks Written in C
Nice work! I would recommend to look into implementing automatic differentiation so that the tool is easily extendible to any differentiable function. For inspiration, I made a scalar valued autodiff implementation a while back repo found here. Making a vector valued implementation would not be too difficult and would be more in line with tensorflow/pytorch api
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A C++ version of Micrograd!
Very nice implementation! I recently did an autodiff implementation myself in C. So it is interesting to see how you did it in C++. If you wanna take a look at my autodiff implementation, here is the repo.
- Automatic differentiation in C
- [Continued] Autodiff implementation in C
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Autodiff in C (need help fixing memory leak)
Anyways, here is the repo: Autodiff
What are some alternatives?
PyMC - Bayesian Modeling and Probabilistic Programming in Python
cpp-micrograd - A c/c++ implementation of micrograd: a tiny autograd engine with neural net on top.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
ggml - Tensor library for machine learning
rstan - RStan, the R interface to Stan
lnn - A Command-Line Program of Feedforward Neural Networks
Elo-MMR - Skill estimation systems for multiplayer competitions
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
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