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

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

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

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

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

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

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

WIP successor to Soss.jl

E.T. Jaynes home phone.