{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Pyro is a deep probabilistic programming library built on PyTorch that enables you to define, fit, and sample from arbitrary probability distributions using composable abstractions for generative and inference models.","skillfed_tags":["bayesian-inference","probabilistic-modeling","pytorch-based"],"use_cases":["Build Bayesian regression or classification models where you need posterior distributions over parameters, not just point estimates.","Perform approximate inference on complex generative models using variational inference or other automated inference strategies.","Design and run experimental designs or sensitivity analyses by sampling from posterior predictive distributions.","Model time-to-event data or other censored observations in survival analysis or reliability engineering.","Prototype custom probabilistic models for scientific research without writing low-level inference code from scratch."],"what_it_does":"Pyro is a probabilistic programming library that lets you express Bayesian models and perform inference on them using PyTorch as its computational backbone. It abstracts away the complexity of building custom inference algorithms by providing high-level primitives for sampling, conditioning, and inference, while still allowing expert users to customize inference strategies when needed.\n\nThe library is designed around the principle that you write generative models as ordinary Python functions, then use Pyro's inference engines to fit those models to data. It handles both discrete and continuous random variables, supports automatic differentiation for gradient-based inference, and scales to large datasets. The five core runtime dependencies (numpy, opt-einsum, pyro-api, torch, tqdm) keep the footprint minimal while providing the numerical and progress-tracking infrastructure needed for practical probabilistic modeling.","worth_installing":"Yes. Pyro is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It is the right choice if you need to build or fit probabilistic models in Python and want a mature, well-documented framework that abstracts away inference complexity while remaining flexible for expert customization."},"id":"pyro-ppl","links":{"html":"https://skillfed.io/packages/pyro-ppl","md":"https://skillfed.io/packages/pyro-ppl.md","pypi":"https://pypi.org/project/pyro-ppl/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-06-02","license_spdx":null,"license_treatment":"permissive","name":"pyro-ppl","python_support":"supports_current","summary":"A Python library for probabilistic modeling and inference"},"popularity":{"monthly_downloads":1232081,"position":4186,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.9.1"}