Causal Inference using Gaussian Processes with Structured Latent Confounders. Estimate treatment effects with Gaussian processes.
MIT License
Plots of Gaussian processes with AbstractGPs and Makie
Estimation of calibration errors.
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) ...
Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning...
Fast inference for Gaussian processes in problems involving time. Partly built on results from ht...
Graphical tools for Bayesian inference and posterior predictive checks
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Bayesian Generalized Linear models using `@formula` syntax.
Estimation and hypothesis tests of calibration in Python using CalibrationErrors.jl and Calibrati...
Approximate variational inference in Julia
A Julia package for Cluster Validity Indices (CVIs).
Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.
A Julia framework for invertible neural networks
Algorithms for quantifying associations, independence testing and causal inference from data.
Multi-language suite for analyzing calibration of probabilistic predictive models.