probabilistic-programming

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 domain-specific language (DSL) for dynamic probabilistic programming

Abstract types and methods for Gaussian Processes.

A domain-specific probabilistic programming language for scalable Bayesian data cleaning

Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

High-performance reactive message-passing based Bayesian inference engine

Sleek implementations of the ZigZag, Boomerang and other assorted piecewise deterministic Markov processes for Markov Chain Monte Carlo including Sticky PDMPs for variable selection

Preheat your MCMC

A domain specific language (DSL) for probabilistic graphical models

WIP successor to Soss.jl

Probabilistic programming for latent Gaussian models in Julia. INLA, TMB, and HMC-Laplace, all in one.

Automatically convert Julia methods to Gen functions.

Implementations of parallel tempering algorithms to augment samplers with tempering capabilities

Automatic probabilistic programming for scientific machine learning and dynamical models

Common types and interfaces for probabilistic programming