stan
pyro
stan | pyro | |
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44 | 9 | |
2,609 | 8,590 | |
0.6% | 0.6% | |
9.5 | 8.0 | |
3 days ago | 4 days ago | |
C++ | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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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/
pyro
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Show HN: Designing Bridges with PyTorch
Mostly I use pytorch for statistical modeling https://pyro.ai . Under the hood that package uses a lot of Monte Carlo integration and variational methods (i.e. integration by optimization). It does support neural nets, but probably >80% of pyro users stick to simpler hierarchical Bayesian models.
- Pyro: The Universal, Probablistic Programming Language
- The Jupyter+Git problem is now solved
- Pyro: Deep universal probabilistic programming with Python and PyTorch
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Computational Bayesian Inference Techniques
Amortized Variational Inference (Like done in pyro.ai with neural networks)
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[P] torchegranate: a PyTorch rewrite of the pomegranate library for probabilistic modeling
Can you compare this to Pyro, which is also built on top of PyTorch?
- [Q] Updated book or review paper on MCMC methods
- Is anyone here working in uncertainty estimation in neural networks?
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[D] Do you train and deploy models using just one framework or multiple frameworks at work?
Using pyod, statmodels, scikit-learn, Tensorflow and pyro.ai (that is using PyTorch as backend). I always use the same framework for training and for production.
What are some alternatives?
PyMC - Bayesian Modeling and Probabilistic Programming in Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
scikit-learn - scikit-learn: machine learning in Python
rstan - RStan, the R interface to Stan
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
trueskill - An implementation of the TrueSkill rating system for Python
Elo-MMR - Skill estimation systems for multiplayer competitions
probability - Probabilistic reasoning and statistical analysis in TensorFlow
Keras - Deep Learning for humans