Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.

Bayesian inference with probabilistic programming.

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

A Bayesian Analysis Toolkit in Julia

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

Probabilistic Programming with Gaussian processes in Julia

Probabilistic programming via source rewriting

Causal, Higher-Order, Probabilistic Programming

A general-purpose probabilistic programming system with programmable inference

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

Probabilistic Circuits from the Juice library

"Distributions" that might not add to one.

E.T. Jaynes home phone.

WIP successor to Soss.jl

Julia package for automated Bayesian inference on a factor graph with reactive message passing

BridgeStan provides efficient in-memory access through Python, Julia, and R to the methods of a Stan model.