Bayesian Modeling and Probabilistic Programming in Python
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Updated
Aug 15, 2024 - Python
Bayesian Modeling and Probabilistic Programming in Python
Deep universal probabilistic programming with Python and PyTorch
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
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.
PyStan, the Python interface to Stan
BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
Lightwood is Legos for Machine Learning.
Gaussian processes in JAX.
Express & compile probabilistic programs for performant inference on CPU & GPU. Powered by JAX.
ProbLog is a Probabilistic Logic Programming Language for logic programs with probabilities.
Functional tensors for probabilistic programming
Oryx is a library for probabilistic programming and deep learning built on top of Jax.
Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
Probabilistic Programming and Nested sampling in JAX
Probabilistic Programming Language for Order Execution and Routing Modeling
Probabilistic data structures in python http://pyprobables.readthedocs.io/en/latest/index.html
Bayesian models to compute performance and uncertainty of returns and alpha.
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