Probabilistic modeling and inference are core tools in diverse fields including statistics, machine learning, computer vision, cognitive science, robotics, natural language processing, and artificial intelligence. To meet the functional requirements of applications, practitioners use a broad range of modeling techniques and approximate inference algorithms. However, implementing inference algorithms is often difficult and error prone. Gen simplifies the use of probabilistic modeling and inference by providing modeling languages in which users express models, and high-level programming constructs that automate aspects of inference.
Like some probabilistic programming research languages, Gen includes universal modeling languages that can represent any model, including models with stochastic structure, discrete and continuous random variables, and simulators. However, Gen is distinguished by the flexibility that it affords to users for customizing their inference algorithm. It is possible to use built-in algorithms that require only a couple lines of code, as well as develop custom algorithms that are more able to meet scalability and efficiency requirements.
Gen’s flexible modeling and inference programming capabilities unify symbolic, neural, probabilistic, and simulation-based approaches to modeling and inference, including causal modeling, symbolic programming, deep learning, hierarchical Bayesian modeling, graphics and physics engines, and planning and reinforcement learning.
Gen is a package for the Julia programming language. Gen consists of multiple modeling languages that are implemented as DSLs in Julia and a Julia library for inference programming.
Please see the Gen home page for more information.