Papers › BlackJAX: Composable Bayesian inference in JAX

BlackJAX: Composable Bayesian inference in JAX

16 Feb 2024arXiv:2402.10797archive 2025-07-28

Alberto Cabezas, Adrien Corenflos, Junpeng Lao, Rémi Louf, Antoine Carnec, Kaustubh Chaudhari, Reuben Cohn-Gordon, Jeremie Coullon, Wei Deng, Sam Duffield, Gerardo Durán-Martín, Marcin Elantkowski, Dan Foreman-Mackey, Michele Gregori, Carlos Iguaran, Ravin Kumar, Martin Lysy, Kevin Murphy, Juan Camilo Orduz, Karm Patel, Xi Wang, Rob Zinkov

BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by taking a functional approach to the algorithms' implementation. BlackJAX is written in Python, using JAX to compile and run NumpPy-like samplers and variational methods on CPUs, GPUs, and TPUs. The library integrates well with probabilistic programming languages by working directly with the (un-normalized) target log density function. BlackJAX is intended as a collection of low-level, composable implementations of basic statistical 'atoms' that can be combined to perform well-defined Bayesian inference, but also provides high-level routines for ease of use. It is designed for users who need cutting-edge methods, researchers who want to create complex sampling methods, and people who want to learn how these work.

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Bayesian InferenceProbabilistic ProgrammingVariational Inference

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Variational Inference

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