{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/5"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/4"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/7"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"TensorFlow Probability provides probabilistic modeling, statistical inference, and Bayesian machine learning tools integrated with TensorFlow, including distributions, variational inference, MCMC sampling, and neural network layers with uncertainty quantification.","skillfed_tags":["probabilistic-modeling","bayesian-inference","uncertainty-quantification"],"use_cases":["Build Bayesian neural networks with uncertainty over weights and predictions for safety-critical applications.","Perform hierarchical statistical modeling and inference on grouped or nested data using joint distributions.","Implement variational autoencoders and other deep generative models with probabilistic layers.","Run MCMC sampling for posterior inference in complex probabilistic models.","Estimate covariances, mixture models, and other statistical quantities with automatic differentiation."],"what_it_does":"TensorFlow Probability is a library for probabilistic reasoning and statistical analysis built on top of TensorFlow. It provides a layered toolkit: distributions with broadcasting semantics, bijectors for transforming random variables, joint distributions for modeling dependencies, probabilistic neural network layers, and inference algorithms including MCMC, variational inference, and Monte Carlo methods. The library integrates automatic differentiation for gradient-based inference and supports hardware acceleration via GPUs and distributed computation.\n\nIt is designed for Bayesian machine learning workflows where you need to model uncertainty, perform hierarchical inference, or combine probabilistic methods with deep networks. The package also works as a JAX substrate, allowing the same probabilistic abstractions to run on JAX instead of TensorFlow. It is actively maintained but carries Beta status, meaning its public interfaces may change.","worth_installing":"Yes, if you need probabilistic modeling or Bayesian inference in a TensorFlow workflow. The library is actively maintained, has low install friction, carries no security vulnerabilities, and offers a comprehensive toolkit for uncertainty quantification and statistical learning. Install with caution only if TensorFlow is not already a dependency in your project, since it adds a substantial indirect dependency; verify that the Beta status and potential interface changes align with your stability requirements."},"id":"tensorflow-probability","links":{"html":"https://skillfed.io/packages/tensorflow-probability","md":"https://skillfed.io/packages/tensorflow-probability.md","pypi":"https://pypi.org/project/tensorflow-probability/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-11-08","license_spdx":null,"license_treatment":"permissive","name":"tensorflow-probability","python_support":"supports_current","summary":"Probabilistic modeling and statistical inference in TensorFlow"},"popularity":{"monthly_downloads":1223637,"position":4195,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.25.0"}