aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Probabilistic reasoning and statistical analysis in TensorFlow
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.
Notebooks about Bayesian methods for machine learning
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
High-quality implementations of standard and SOTA methods on a variety of tasks.
A simple probabilistic programming language.
A collection of Bayesian data analysis recipes using PyMC3
rstanarm R package for Bayesian applied regression modeling
Data Assimilation with Python: a Package for Experimental Research
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
shinystan R package and ShinyStan GUI
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Probabilistic Inference on Noisy Time Series
A Python package for building Bayesian models with TensorFlow or PyTorch
Statistical Rethinking with PyTorch and Pyro
Statistical Rethinking course in pymc3
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