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.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

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

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

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

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

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

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

Abstract types and interfaces for Markov chain Monte Carlo methods

High level functions for analyzing the output of simulations

Preheat your MCMC

Bayesian Generalized Linear models using `@formula` syntax.

Turing + Makie = Turkie

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks

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Implementations of single and multi-ellipsoid nested sampling

Methods for computational information geometry

Common functions in actuarial and financial routines

Comparing performance and results of mcmc options using Julia

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

Composable contracts, models, and functions that allow for modeling of both simple and complex financial instruments

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

Implementations of parallel tempering algorithms to augment samplers with tempering capabilities

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

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

Common types and interfaces for probabilistic programming

Random Integrators for many-body quantum systems

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

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia