Bayesian Inference
Showing 26 of 26 projects
An introduction to Bayesian inference and probabilistic programming using Python and PyMC, with a computational-first approach.
A comprehensive collection of machine learning algorithms implemented exclusively in NumPy for educational purposes and prototyping.
A flexible, scalable deep probabilistic programming library built on PyTorch for universal representation of computable probability distributions.
A flexible, scalable deep probabilistic programming library built on PyTorch for universal probabilistic modeling.
A Python package for constrained global optimization using Bayesian inference and Gaussian processes.
A library for probabilistic reasoning and statistical analysis integrated with TensorFlow and JAX.
A highly efficient, scalable Gaussian process library implemented in PyTorch with GPU acceleration and modular design.
A lightweight probabilistic programming library using NumPy and JAX for autograd and JIT compilation to GPU/TPU/CPU.
An open-source Python library for probabilistic time series modeling with both frequentist and Bayesian inference methods.
A fast, modular Bayesian inference library for JAX, providing composable samplers for CPU and GPU.
A Python library implementing Factorization Machines with a scikit-learn compatible API for regression, classification, and ranking tasks.
A Python library for probabilistic state space modeling and inference, built on JAX.
A probabilistic programming language built on Scala for creating rich probabilistic models and performing automated reasoning.
A low-level Gaussian process framework in JAX and Flax, designed for maximum flexibility and close alignment with mathematical notation.
A Python library for Bayesian inference in Hidden Markov Models (HMMs) and Hidden semi-Markov Models (HSMMs) with nonparametric extensions.
A Clojure library for high-performance Bayesian data analysis and machine learning on the GPU.
A Python package for training PyTorch neural networks using variational inference for Bayesian deep learning.
An extremely lightweight Gaussian Process library for Python built on JAX with GPU acceleration and automatic differentiation.
Real-time Bayesian terrain traversability mapping and motion planning system for ROS-compatible unmanned ground vehicles using LiDAR point clouds.
A JAX-powered probabilistic programming library focused on performant sampling methods for Bayesian inference on CPU, GPU, and TPU.
A Julia package for Gaussian process modeling with support for exact inference, MCMC sampling, and non-Gaussian likelihoods.
A parallel Monte Carlo and machine learning library for scientific inference, available in Python, MATLAB, Fortran, C++, and C.
A JAX-based probabilistic programming framework using nested sampling for fast Bayesian inference and evidence computation.
A Python probabilistic programming framework for objective model selection in time-varying parameter time series models.
A full-featured Ruby implementation of Naive Bayes for probabilistic classification with customizable features.
Bayesian inference tools in Python for estimating Dirichlet priors and multinomial mixture models from discrete event data.
Related Tags
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.