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Top 23 bayesian-inference Open-Source Projects
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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.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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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.
Project mention: Stan: Statistical modeling and high-performance statistical computation | news.ycombinator.com | 2024-03-04 -
is used to capture the power of a fully-trained deep net of infinite width.
https://openreview.net/pdf?id=rkl4aESeUH, https://github.com/google/neural-tangents
> It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width.
https://arxiv.org/abs/1711.00165
And of course, one needs to look back at SVMs applying a kernel function and separating with a line, which looks a lot like an ANN with a single hidden layer followed by a linear mapping.
https://stats.stackexchange.com/questions/238635/kernel-meth...
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causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
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numpyro
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
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As it happens, there's a PyMC implementation of the 1st and 2nd editions of Statistical Rethinking here:
https://github.com/pymc-devs/pymc-resources
(I think the author of the book discussed above, Osvaldo Martin, is the primary or sole contributor for the Rethinking implementations, in fact -- he had a full implementation in his own repo [here](https://github.com/aloctavodia/Statistical-Rethinking-with-P...) before deprecating it in favor of the above-linked one.)
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Project mention: Rxinfer: Automatic Bayesian Inference Through Reactive Message Passing | news.ycombinator.com | 2023-11-24
A closer library is Infer.NET: https://dotnet.github.io/infer
It includes a really mature compiler that generates very efficient message passing and variational inference, with support for online inference, which is the main focus on Rxinfer.
You can call Infer.NET from Python in a number of ways, despite it is not a CPython library.
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Project mention: Bayesian Structural Equation Modeling using blavaan | news.ycombinator.com | 2023-11-09
[2] https://paul-buerkner.github.io/brms/
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Project mention: Bayesian Structural Equation Modeling using blavaan | news.ycombinator.com | 2023-11-09
It is much less challenging with Bambi[1] and brms[2].
[1] https://bambinos.github.io/bambi/
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DSGE.jl
Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
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What are the other four frameworks?
> For one, who wants to do stuff in tensorflow anymore let alone tensorflow-probability.
AutoBNN is a JAX library and has nothing to do technically with TF Probability. It was developed by the TF Probability team.
> DL community prefers pytorch and stats community prefers Stan.
It looks like the JAX ecosystem for stats is growing: NumPyro is based on JAX, PyMC has a JAX backend, https://github.com/blackjax-devs/blackjax has effective samplers, there is https://github.com/jax-ml/bayeux, and now AutoBNN.
> This one seems theoretically more interesting than some others but practically less useful.
Are there other factors why you think AutoBNN is not practically useful, apart from being based on the wrong foundation (which was a mistaken belief of yours)?
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pytensor
PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.
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paramonte
ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
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Project mention: A resource list for causality in statistics, data science and physics | news.ycombinator.com | 2023-09-02
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bayesian-inference related posts
- Stan: Statistical modeling and high-performance statistical computation
- Bayesian Analysis with Python
- Rxinfer: Automatic Bayesian Inference Through Reactive Message Passing
- Bayesian Structural Equation Modeling using blavaan
- A resource list for causality in statistics, data science and physics
- Ask HN: What Are You Learning?
- How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have?
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Index
What are some of the best open-source bayesian-inference projects? This list will help you:
Project | Stars | |
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1 | pyro | 8,342 |
2 | PyMC | 8,125 |
3 | stan | 2,515 |
4 | neural-tangents | 2,217 |
5 | causalnex | 2,135 |
6 | numpyro | 2,025 |
7 | pymc-resources | 1,877 |
8 | infer | 1,534 |
9 | awesome-normalizing-flows | 1,292 |
10 | brms | 1,228 |
11 | bambi | 1,009 |
12 | rstan | 1,001 |
13 | fortuna | 845 |
14 | DSGE.jl | 842 |
15 | blackjax | 709 |
16 | Stheno.jl | 332 |
17 | pytensor | 240 |
18 | paramonte | 236 |
19 | looper | 235 |
20 | vbmc | 210 |
21 | indaba-pracs-2022 | 172 |
22 | InvertibleNetworks.jl | 141 |
23 | cobaya | 114 |