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the tiniest of Gaussian Process libraries
The main features of dfm/tinygp are: Probabilistic Programming.
Open-source alternatives to dfm/tinygp include: pyro-ppl/pyro — Pyro is a deep probabilistic programming library and differentiable probabilistic modeler designed for Bayesian… uber/pyro — Pyro is a probabilistic programming language and library built for PyTorch. It serves as a Bayesian inference engine… camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers — This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for… facebookincubator/prophet — Prophet is a predictive analytics framework and time series regression library designed for forecasting future values.… joshuaalbert/jaxns — Main Status:. blackjax-devs/blackjax — BlackJAX is a library of samplers for JAX that works on CPU as well as GPU.
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Prophet is a predictive analytics framework and time series regression library designed for forecasting future values. It uses additive models to fit non-linear growth and periodic seasonal patterns, providing tools for producing forecasts with integrated error measurement. The project handles multiple seasonalities and holiday effects to improve accuracy for periodic data. It supports the integration of external regressors and manages data irregularities, such as missing data and outliers, to maintain prediction stability. The framework covers a broad range of analysis capabilities, includi
This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for performing statistical inference and quantifying uncertainty through a probabilistic programming workflow using Python. The resource employs a computation-first pedagogy, teaching Bayesian methods and parameter estimation through executable code and simulations instead of formal mathematical notation. It provides a practical approach to implementing Markov Chain Monte Carlo sampling to estimate posterior distributions. The content covers building probabilistic models, integrating e
Pyro is a probabilistic programming language and library built for PyTorch. It serves as a Bayesian inference engine and a tool for probabilistic graphical modeling, allowing users to define generative models that combine neural networks with probabilistic logic. The framework enables deep probabilistic programming by integrating probability distributions into computational graphs. This allows for the quantification of uncertainty in deep learning models and the execution of scalable posterior distribution calculations for complex data dependencies. The system provides a suite of inference c