Network Hawkes processes in Julia.
MIT License
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Probabilistic programming via source rewriting
MCMC Inference for a Hawkes process in Julia
A statistical toolbox for diffusion processes and stochastic differential equations. Named after ...
[draft] Agnostic Machine Learning models working on CPUs, GPUs, distributed architecture, etc.
Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning...
A Julia package that implements a category of reaction (transportation) network-type dynamical sy...
Simple & fast linear regression in Julia
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) ...
Algorithms for quantifying associations, independence testing and causal inference from data.
Examples for Bayesian inference using DynamicHMC.jl and related packages.
A Julia framework for invertible neural networks
A decision-making framework for the cost-efficient design of experiments, balancing the value of ...
A unified interface for simulating and evaluating sequential sampling models in Julia.
A framework for composing Neural Processes in Julia