#Gaussian process

GPflow facilitates building Gaussian process models in Python utilizing TensorFlow 2.4+ and TensorFlow Probability, optimized for GPU computations. This open-source platform adheres to contemporary inference methods with customizable kernels and likelihoods. The project benefits from community participation, with enhancements from numerous contributors. Detailed documentation and a supportive Slack community assist in smooth adoption and application. Users can opt for stable releases or experiment with the latest features in GPflow, offering flexibility and swift deployment capabilities.

GPyTorch is a scalable and flexible library for Gaussian processes built on PyTorch. It uses numerical linear algebra for efficient inference and supports GPU acceleration, making it suitable for integration with deep learning frameworks. Contributions include advancements in techniques like SKI/KISS-GP and stochastic variational deep kernel learning. Support is available for Python 3.8 and higher, with easy installation via pip or conda. The library is developed with input from significant contributors, enabling strong Gaussian process solutions for diverse applications.