We present a new approach to the design and implementation of probabilistic programming languages (PPLs), based on idea stochastically estimating probability density ratios necessary for inference. By relaxing usual PPL constraint that these densities be computed exactly, we are able eliminate many common restrictions in current PPLs, deliver language that, first time, simultaneously supports f...