Showing 7 of 7 projects
A flexible, scalable deep probabilistic programming library built on PyTorch for universal probabilistic modeling.
A flexible, scalable deep probabilistic programming library built on PyTorch for universal representation of computable probability distributions.
A library for probabilistic reasoning and statistical analysis integrated with TensorFlow and JAX.
A lightweight probabilistic programming library using NumPy and JAX for autograd and JIT compilation to GPU/TPU/CPU.
A Python package providing Bayesian machine learning algorithms with a scikit-learn compatible API.
A Python package for training PyTorch neural networks using variational inference for Bayesian deep learning.
A JAX-based framework for approximate inference in Markov Gaussian processes using iterated Kalman smoothing.
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