tensorflow/probability

Health v2 · maintenance only

Adoption not part of the score

4427 stars · 1124 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TensorFlow Probability is a Python library for probabilistic reasoning and statistical analysis built on TensorFlow, with a JAX substrate. It provides probability distributions, bijectors, joint distribution modeling, probabilistic neural network layers, and inference tools like MCMC and variational inference.

Use cases

• fit bayesian regression models with uncertainty estimates
• run MCMC sampling for probabilistic models
• build deep probabilistic models with probabilistic keras layers
• model distributions and transformed random variables with bijectors
• do variational inference on large datasets with GPU acceleration
• perform probabilistic programming in JAX
• fit GLMs like logistic regression in TensorFlow

When to choose

• you need probabilistic or Bayesian modeling integrated with deep learning
• you want scalable inference on GPU/TPU hardware
• you need a rich library of distributions, bijectors, and samplers
• you work in the TensorFlow or JAX ecosystem

When to avoid

• you only need classical statistics without deep learning integration
• your project uses PyTorch instead of TensorFlow/JAX
• you need lightweight statistical tools without a heavy framework dependency

Facets

library · maturity active

machine-learning math data-science machine-learning deep-learning data-science python cross-platform probabilistic-programming bayesian-inference mcmc variational-inference tensorflow jax distributions bijectors statistics gpu

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markdown · JSON · MCP: product_card(name="tensorflow/probability")