Coix: A JAX-based AI Framework Designed for Composing Probabilistic Programs and Performing Inference on Them

In probabilistic programming, developers often face the challenge of efficiently composing and performing inference on intricate probabilistic programs. A recent release, Coix (COmbinators In jaX), has emerged as a flexible and backend-agnostic solution to address this. Coix offers a comprehensive set of program transformations known as inference combinators, enabling compositional inference with probabilistic programs.

One of Coix’s standout features is its support for multiple backends, including numpyro and oryx. This versatility allows developers to choose the backend that best fits their needs and seamlessly switch between them as necessary. Moreover, Coix comes equipped with a range of pre-implemented losses and utility functions, empowering users to effortlessly implement and execute various inference algorithms straight out of the box.

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The framework comprises several main components, each serving a specific purpose. 

  • coix.api module: Implements program combinators, providing a high-level interface for composing probabilistic programs.
  • coix.core module: Provides basic program transformations to modify the behavior of stochastic programs, increasing their flexibility and adaptability.
  • coix.loss module: Offers common objectives for variational inference, simplifying the process of optimizing probabilistic models.
  • coix.algo module: This module includes example inference algorithms, serving as valuable resources for developers looking to explore and understand the framework’s capabilities.

With its modular architecture, Coix facilitates the seamless integration of additional backends via the coix.register_backend utility. This extensibility ensures that the framework remains adaptable to evolving requirements and preferences within the probabilistic programming community.

In conclusion, Coix represents a significant advancement in probabilistic programming. It offers a user-friendly and versatile framework for composing probabilistic programs and easily performing inference. With its rich feature set, support for multiple backends, and emphasis on flexibility and extensibility, Coix is poised to become a valuable tool for researchers and practitioners alike in probabilistic modeling and inference.

Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.

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