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

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

Abstract types and methods for Gaussian Processes.

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

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

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

High-performance reactive message-passing based Bayesian inference engine

WIP successor to Soss.jl

Preheat your MCMC

Automatically convert Julia methods to Gen functions.

Automatic probabilistic programming for scientific machine learning and dynamical models

Implementations of parallel tempering algorithms to augment samplers with tempering capabilities

Common types and interfaces for probabilistic programming

Kernel Density Estimate with product approximation using multiscale Gibbs sampling

Building blocks for simple and advanced particle filtering in Gen.