•1 min read•from Machine Learning
Parax v0.7: Parametric Modeling in JAX [P]
Hi everyone!
Parax is a library for "Parametric modeling" in JAX, attempting to bridge the approach between pure JAX PyTrees, and more object-orientated modeling approaches (e.g. using Equinox).
v0.7 has been released, featuring a more polished API as well as some detailed examples in the documentation.
Some of Parax's features:
- Derived/constrained parameters with metadata
- Computed PyTrees and callable parameterizations
- Abstract interfaces for fixed, bounded, and probabilistic PyTrees and parameters
Two new examples in the docs that show off these features
- Bounded optimization (JAXopt)
- Bayesian sampling (BlackJAX)
Perhaps the library is of use to someone, and feel free to leave any feedback!
Cheers,
Gary
[link] [comments]
Want to read more?
Check out the full article on the original site
Tagged with
#financial modeling with spreadsheets
#financial modeling
#natural language processing for spreadsheets
#generative AI for data analysis
#rows.com
#Excel alternatives for data analysis
#spreadsheet API integration
#Parax
#Parametric modeling
#JAX
#PyTrees
#Equinox
#API
#derived parameters
#constrained parameters
#metadata
#callable parameterizations
#abstract interfaces
#fixed PyTrees
#bounded PyTrees