Implicit probabilistic models are a flexible class for modeling data. They define a process to simulate observations, and unlike traditional models, they do not require a tractable likelihood function. In this paper, we develop two families of models: hierarchical implicit models and deep implicit models. They combine the idea of implicit densities with hierarchical Bayesian modeling and deep neural networks. The use of implicit models with Bayesian analysis has been limited by our ability to perform accurate and scalable inference. We develop likelihood-free variational inference (LFVI). Key to LFVI is specifying a variational family that is also implicit. This matches the model's flexibility and allows for accurate approximation of the posterior. Our work scales up implicit models to sizes previously not possible and advances their modeling design. We demonstrate diverse applications: a large-scale physical simulator for predator-prey populations in ecology; a Bayesian generative adversarial network for discrete data; and a deep implicit model for text generation.
Likelihood-free variational inference (LFVI) REF , merge this concept with learning implicit densities using hierarchical Bayesian modeling.
Deep and Hierarchical Implicit Models
ArXiv
ArXiv
Mathematics Computer Science