Probabilistic Programming
Showing 22 of 22 projects
An introduction to Bayesian inference and probabilistic programming using Python and PyMC, with a computational-first approach.
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 library for probabilistic reasoning and statistical analysis integrated with TensorFlow and JAX.
A lightweight probabilistic programming library using NumPy and JAX for autograd and JIT compilation to GPU/TPU/CPU.
A Python tutorial and cookbook for implementing Bayesian modeling techniques using PyMC3.
A Python library for exploratory analysis, diagnostics, and visualization of Bayesian models.
A neuro-symbolic Python framework that combines classical programming with LLMs through composable primitives and design-by-contract validation.
A Go library of probabilistic data structures for processing continuous, unbounded data streams.
A fast, modular Bayesian inference library for JAX, providing composable samplers for CPU and GPU.
A Swift library providing probability tools and advanced data structures for building intelligent iOS/macOS applications.
A probabilistic programming language built on Scala for creating rich probabilistic models and performing automated reasoning.
A JAX-native library of probability distributions and bijectors, reimplementing a subset of TensorFlow Probability with emphasis on readability and extensibility.
A low-level Gaussian process framework in JAX and Flax, designed for maximum flexibility and close alignment with mathematical notation.
A tensor library for differentiable functional programming in F#, with PyTorch-like APIs and GPU support.
An AutoML framework that generates and customizes machine learning pipelines using declarative JSON-AI syntax.
A Clojure library for high-performance Bayesian data analysis and machine learning on the GPU.
A JAX-powered probabilistic programming library focused on performant sampling methods for Bayesian inference on CPU, GPU, and TPU.
A Julia package for implementing and applying Markov chain Monte Carlo (MCMC) methods for Bayesian analysis.
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 Rocq library providing a formalized hierarchy of monads and their laws for monadic equational reasoning.
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