http://arxiv.org/abs/1803.06473
Many problems in Astrophysics involve using Bayesian Inference to deal with problems of parameter estimation and model selection. In this paper, we introduce Variational Inference to solve these problems and compare how the results hold up to Markov Chain Monte Carlo which is the most common method. Variational Inference converts the inference problem into an optimization problem by approximating the posterior from a known family of distributions and using Kullback-Leibler divergence to measure closeness. Variational Inference takes advantage of fast optimization techniques which make it ideal to deal with large datasets and also makes it trivial to parallelize. As a proof of principle, we apply Variational Inference for parameter estimation and model comparison to four different problems in astrophysics where MCMC techniques were previously used: measuring exoplanet orbital parameters from radial velocity data, tests of periodicities in measurements of $G$, significance of a turnover in the spectral lag data of GRB 160625B , and estimating the mass of a galaxy cluster using weak lensing. We find that Variational Inference is much faster than MCMC for these problems.
A. Jain, P. Srijith and S. Desai
Tue, 20 Mar 2018
15/68
Comments: 12 pages, 3 figures