stan
MultiBUGS
stan | MultiBUGS | |
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44 | 1 | |
2,609 | 33 | |
0.6% | - | |
9.5 | 0.0 | |
5 days ago | over 3 years ago | |
C++ | Shell | |
BSD 3-clause "New" or "Revised" License | GNU Lesser General Public License v3.0 only |
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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/
MultiBUGS
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Project Oberon Emulator in JavaScript and Java
Oberon is awesome. I typically use a variant of it called Component Pascal. It is a very esoteric, and quite unique language.
My usage of it is in WinBUGS/OpenBUGS/MultiBUGS [1], for Markov chain Monte Carlo statistical analysis. It's really cool and works amazingly well for systems of differential equations too.
The version I recommend using is MultiBUGS [2]. I would avoid installing it in Windows, though!
[1] https://www.mrc-bsu.cam.ac.uk/software/bugs/
[2] https://github.com/MultiBUGS/MultiBUGS
What are some alternatives?
PyMC - Bayesian Modeling and Probabilistic Programming in Python
rstan - RStan, the R interface to Stan
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
paramonte - ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
numpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
security - Collection of CVEs from Sick Codes, or collaborations on https://sick.codes security research & advisories.
Elo-MMR - Skill estimation systems for multiplayer competitions
probability - Probabilistic reasoning and statistical analysis in TensorFlow
rnim - A bridge between R and Nim