Bayesian inference with probabilistic programming.

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

Implementation of normalising flows and constrained random variable transformations

Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Robust implementation for random-walk Metropolis-Hastings algorithms

Abstract types and interfaces for Markov chain Monte Carlo methods

Dynamically track IR as a graph, using source transformations

A library for variational Bayesian methods in Julia

A Julia interface for PyTorch's C++ backend, focusing on Tensor, AD, and JIT

Implementations of single and multi-ellipsoid nested sampling

Advanced sequential Monte Carlo and particle MCMC sampling library in Julia

C shim for task copying in Turing

Julia implementation of elliptical slice sampling.

Common interfaces for probabilistic programming

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MCMCDebugging.jl: debugging utilities for MCMC samplers