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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.10797 |
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| _version_ | 1866913239558782976 |
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| author | Cabezas, Alberto Corenflos, Adrien Lao, Junpeng Louf, Rémi Carnec, Antoine Chaudhari, Kaustubh Cohn-Gordon, Reuben Coullon, Jeremie Deng, Wei Duffield, Sam Durán-Martín, Gerardo Elantkowski, Marcin Foreman-Mackey, Dan Gregori, Michele Iguaran, Carlos Kumar, Ravin Lysy, Martin Murphy, Kevin Orduz, Juan Camilo Patel, Karm Wang, Xi Zinkov, Rob |
| author_facet | Cabezas, Alberto Corenflos, Adrien Lao, Junpeng Louf, Rémi Carnec, Antoine Chaudhari, Kaustubh Cohn-Gordon, Reuben Coullon, Jeremie Deng, Wei Duffield, Sam Durán-Martín, Gerardo Elantkowski, Marcin Foreman-Mackey, Dan Gregori, Michele Iguaran, Carlos Kumar, Ravin Lysy, Martin Murphy, Kevin Orduz, Juan Camilo Patel, Karm Wang, Xi Zinkov, Rob |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10797 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BlackJAX: Composable Bayesian inference in JAX Cabezas, Alberto Corenflos, Adrien Lao, Junpeng Louf, Rémi Carnec, Antoine Chaudhari, Kaustubh Cohn-Gordon, Reuben Coullon, Jeremie Deng, Wei Duffield, Sam Durán-Martín, Gerardo Elantkowski, Marcin Foreman-Mackey, Dan Gregori, Michele Iguaran, Carlos Kumar, Ravin Lysy, Martin Murphy, Kevin Orduz, Juan Camilo Patel, Karm Wang, Xi Zinkov, Rob Mathematical Software Machine Learning Computation 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. |
| title | BlackJAX: Composable Bayesian inference in JAX |
| topic | Mathematical Software Machine Learning Computation |
| url | https://arxiv.org/abs/2402.10797 |