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Stan Alternatives
Similar projects and alternatives to stan
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jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
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Sonar
Write Clean C++ Code. Always.. Sonar helps you commit clean C++ code every time. With over 550 unique rules to find C++ bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
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brms
brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
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InfluxDB
Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.
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fastbaps
A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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tests-as-linear
Common statistical tests are linear models (or: how to teach stats)
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Stan.jl
Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.
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pytensor
PyTensor is a fork of Aesara -- a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better stan alternative or higher similarity.
stan reviews and mentions
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 2023-05-11.
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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
- Automatic differentiation in C
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[D] Programming language for developing computational statistics algorithms
I'd say take a look at Stan (https://mc-stan.org/)
Well it sounds a lot like you are listening to developers talk about coding languages they like for high performance compute. This is not what you want to be spending all your time doing afaik. The more appropriate languages to get into would be the classic Python and R. Julia if you dont give a shit about productionizing your code and https://mc-stan.org/ Stan if you are really locked into bayesian inference and wanted to pick julia anyway.
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Is python necessary to learn machine learning?
Even if RStudio & the Tidyverse have mostly been promoting a functional programming style in R, it has full support for OOP (see R6 or R7 for more modern implementations of it). Let's not even mention the excellent Stan ecosystem for Probabilistic programming / Bayesian modeling, or Bioconductor, the biggest repository of bioinformatics packages & tools of any language.
- [Q] Updated book or review paper on MCMC methods
- Stan in Nim?
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Step-by-step example of Bayesian t-test?
Okay so first off, I recommend that you read [this](https://link.springer.com/article/10.3758/s13423-016-1221-4) article about "The Bayesian New Statistics", which highlights estimation rather than hypothesis testing from a Bayesian perspective (see Fig. 1, second row, second column). Instead of a t-test, then, we can *estimate the difference* between two groups/variables. If you want to go deeper than JASP etc, I recommend that you use [brms](https://paul-buerkner.github.io/brms/), or, if you want to go even deeper, [Stan](https://mc-stan.org/) (brms is a front-end to Stan).
The ` sigma ~ x` part specifies that sigma should be estimated separately for each group. Note that I'm also using scaled data, since then I can go by the Stan team's [prior choice recommendations](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations). We also specified ` family = gaussian` , which is telling the model to treat ` y` , the difference between the two variables, as normally distributed. In other words, this is the likelihood! There are [lots](https://cran.r-project.org/web/packages/brms/vignettes/brms_families.html) of "families" in brms. In particular, if you use a Student's t distribution instead, your model will be more robust against outliers!
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Stats
Basic stan repo stats
41
2,416
9.4
3 days ago
stan-dev/stan is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of stan is C++.
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