Bayesian inference with probabilistic programming.

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

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.

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

A Bayesian Analysis Toolkit in Julia

Implementation of normalising flows and constrained random variable transformations

A Julia framework for invertible neural networks

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

State estimation, smoothing and parameter estimation using Kalman and particle filters.

Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html

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

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

WIP successor to Soss.jl

Preheat your MCMC

Distributed and parallel sampling from intractable distributions

Sequential Monte Carlo algorithm for approximation of posterior distributions.