Bayesian inference with probabilistic programming.

A general-purpose probabilistic programming system with programmable inference

Probabilistic programming via source rewriting

"Distributions" that might not add to one.

Probabilistic Programming with Gaussian processes in Julia

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

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

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

A Bayesian Analysis Toolkit in Julia

Causal, Higher-Order, Probabilistic Programming

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

Probabilistic Circuits from the Juice library

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.

E.T. Jaynes home phone.