Bayesian inference with probabilistic programming.

WIP successor to Soss.jl

Probabilistic Programming with Gaussian processes in Julia

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

Probabilistic programming via source rewriting

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

Probabilistic Circuits from the Juice library

Causal, Higher-Order, Probabilistic Programming

"Distributions" that might not add to one.

E.T. Jaynes home phone.

A general-purpose probabilistic programming system with programmable inference

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

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

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

A Bayesian Analysis Toolkit in Julia

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