2020/04/24 by Grigor Aslanyan, Richard Easther, Aslanyan, Grigor +5
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Advanced Data Storage Technologies #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Computer and information sciences #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Gaussian Processes and Bayesian Inference #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Simulation Techniques and Applications #astro-ph.CO #astro-ph.IM #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2004.11929
code available from https://doi.org/10.5281/zenodo.3764460 or https://github.com/auckland-cosmo/LearnAsYouGoEmulator
arxiv created 2020/04/24 · openalex publication_date 2020/04/24 · arxiv updated 2020/04/28 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters. While various emulation schemes exist to approximate these slow calculations, these approaches are only safe if the approximations are well understood and controlled. This workshop submission reviews and updates a previously published method, which has been used in cosmological simulations, to (1) train an emulator while simultaneously estimating posterior probabilities with MCMC and (2) explicitly propagate the emulation error into errors on the posterior probabilities for model parameters. We demonstrate how these techniques can be applied to quickly estimate posterior distributions for parameters of the ΛCDM cosmology model, while also gauging the robustness of the emulator approximation.