A central problem in astronomy is to infer the locations and other latent properties of stars and galaxies appearing in telescopic images. In these images, each pixel records a count of the photons—originating from stars, galaxies, and the background—that entered a particular region of a telescope's lens during an exposure. Each count is well modeled as a Poisson random variable, whose rate parameter is a deterministic function of the latent properties of nearby stars and galaxies. In this talk, I present a generative, probabilistic model of astronomical images, as well as a scalable procedure for inferring the latent properties of imaged stars and galaxies from it. Experimental results suggest that principled probabilistic models are a viable alternative to ad hoc approaches.