Language Integrations

Language Integrations#

This section demonstrates how to integrate nested sampling with various programming languages and frameworks. Each integration shows how to call nested sampling routines from different environments and how to handle the data exchange between JAX/BlackJAX and the target language.

Available Integrations#

The following language integrations are available:

  • PyTorch - Using nested sampling with PyTorch tensors and models

  • Numba - JIT compilation for performance optimization

  • NumPy/SciPy - Integration with NumPy arrays and SciPy functions

  • TensorFlow - Using nested sampling with TensorFlow models

  • CuPy - GPU acceleration with CuPy arrays

  • C - Calling C functions from JAX

  • C++ - Integration with C++ code

  • Fortran - Using Fortran subroutines

  • R - Calling R functions and using R data

  • Julia - Integration with Julia code

Each example includes setup instructions, code examples, and performance considerations for the specific integration.

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