{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/10"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/14"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"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 and JAX, including distributions, bijectors, inference algorithms, and probabilistic neural network layers.","skillfed_tags":["bayesian-inference","probabilistic-modeling","uncertainty-quantification"],"use_cases":["Build Bayesian neural networks with weight uncertainty and quantify prediction confidence","Perform hierarchical statistical modeling and inference on grouped or nested data","Implement Markov chain Monte Carlo or variational inference for posterior approximation","Define and sample from complex joint probability distributions with dependencies","Combine probabilistic layers with standard deep learning models for uncertainty-aware inference","Conduct dimensionality reduction or clustering using probabilistic generative models"],"what_it_does":"TensorFlow Probability is a library for building and reasoning about probabilistic models within the TensorFlow ecosystem. It layers statistical building blocks\u2014distributions, bijectors, and transformations\u2014on top of TensorFlow's numerical operations, then adds higher-level tools for model construction (joint distributions, probabilistic layers) and inference (MCMC, variational inference, stochastic optimizers). The library also works as a JAX substrate, letting you use the same probabilistic API with JAX's functional paradigm instead of TensorFlow's graph execution.\n\nTypical use involves defining a probabilistic model using distributions and layers, then performing inference via gradient-based optimization or sampling-based methods. It is designed for researchers and practitioners building Bayesian neural networks, hierarchical models, and other probabilistic systems that benefit from automatic differentiation and hardware acceleration. The package depends on absl-py, six, numpy, decorator, cloudpickle, gast, and dm-tree.","worth_installing":"Yes, if you are building probabilistic or Bayesian machine learning models in TensorFlow or JAX. The library is actively maintained, has no known vulnerabilities, and integrates with TensorFlow's ecosystem. Install with caution only if your project requires stable APIs, as the package is marked Beta and interfaces may change; verify compatibility with your TensorFlow version for production use."},"id":"tfp-nightly","links":{"html":"https://skillfed.io/packages/tfp-nightly","md":"https://skillfed.io/packages/tfp-nightly.md","pypi":"https://pypi.org/project/tfp-nightly/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"tfp-nightly","python_support":"supports_current","summary":"Probabilistic modeling and statistical inference in TensorFlow"},"popularity":{"monthly_downloads":274867,"position":8184,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.26.0.dev20260814"}