![Parax v0.7: Parametric Modeling in JAX [P]](https://ai-maestro.online/wp-content/uploads/2026/05/parax-v0-7-parametric-modeling-in-jax-p--1024x576.jpg)
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A new version 0.7 of the Parax library has been released, focusing on enhancing its API and providing more detailed documentation examples. Key features include support for derived/constrained parameters with metadata, computed PyTrees, and abstract interfaces for various types of PyTrees and parameters. The release includes two new examples in the documentation: one demonstrating bounded optimization using JAXopt and another showcasing Bayesian sampling with BlackJAX.
- Enhanced API for better usability
- Detailed examples added to the documentation
- Incorporation of features like derived/constrained parameters, computed PyTrees, and abstract interfaces
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This brief highlights the key points of Parax v0.7 release, emphasizing its improved API and new examples in the documentation.
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