Gen

Gen is an open-source framework for probabilistic modeling and inference that automates complex probabilistic inference by providing building blocks for customized algorithms. The framework supports hybrid approaches combining neural networks, variational inference, sequential Monte Carlo, and MCMC methods with dynamic computation graphs for models with stochastic structure. The Julia implementation is available with ports to additional languages underway.