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Approximate the Simulator, Not the Inference: Using Converging Hamiltonian Monte Carlo for Flexible SBI in Astronomy

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Reliably including computationally intensive simulators with intractable densities in an inference problem is of paramount importance in astronomy. In this talk, I present a normalizing flow architecture that can replace intractable simulators in Bayesian models, while guaranteeing fast convergence of the No U-Turn Sampler variant of Hamiltonian Monte Carlo (HMC) to the posterior distribution. I discuss the limitations of using flow-based methods in HMC , and illustrate examples of a correct implementation for toy examples. I conclude by illustrating its use for estimating interstellar extinction from stellar colours.

This talk is part of the Astro Data Science Discussion Group series.

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Mott Seminar Room (531) Room A, West Hub Peter Richards Rm, Hughes Hall, Cambridge CB1 2EW

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