Bayesian inference with probabilistic programming.

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

Types and utility functions for summarizing Markov chain Monte Carlo simulations

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.

Implementations of the models from the Statistical Rethinking book with Turing.jl

Causal, Higher-Order, Probabilistic Programming

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

Gaussian Process package based on data augmentation, sparsity and natural gradients

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

Robust implementation for random-walk Metropolis-Hastings algorithms

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

Stochastic Optimization, Learning, Uncertainty and Sampling

Sampling from intractable distributions, with support for distributed and parallel methods

High level functions for analyzing the output of simulations

Abstract types and interfaces for Markov chain Monte Carlo methods

Preheat your MCMC

Bayesian Generalized Linear models using `@formula` syntax.

Turing + Makie = Turkie

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

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Implementations of single and multi-ellipsoid nested sampling

Examples for Bayesian inference using DynamicHMC.jl and related packages.

Comparing performance and results of mcmc options using Julia

An interface for accelerated simulation of high-dimensional collisionless and electrostatic plasmas.

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Create parameter estimation problems for ODE models

Octofitter is a Julia package for performing Bayesian inference against direct images of exoplanets, relative astrometry, and astrometric acceleration of the host star.

Pure julia implementation of Multiple Affine Invariant Sampling for efficient Approximate Bayesian Computation

Implementations of parallel tempering algorithms to augment samplers with tempering capabilities

A unified interface for simulating and evaluating sequential sampling models in Julia.

Common types and interfaces for probabilistic programming

Data and functions to support Julia projects based on the book "Regression and Other Stories" by Andrew Gelman, Jennifer Hill and Aki Vehtari.

Package for Bayesian and classical estimation and inference based on statistics that are filtered through a trained neural net

The Julia implementation of the generalised hierarchical Gaussian filter

Graphical tools for Bayesian inference and posterior predictive checks

Implementation of domain specific language (DSL) for probabilistic graphical models