Jul 26 – 30, 2021
US/Eastern timezone

Bayesian Model Averaging for Lattice Field Theory

Jul 27, 2021, 2:00 PM
15m
Oral presentation Algorithms (including Machine Learning, Quantum Computing, Tensor Networks) Algorithms (including Machine Learning, Quantum Computing, Tensor Networks)

Speaker

Prof. Ethan Neil (University of Colorado Boulder)

Description

Statistical modeling plays a key role in lattice field theory calculations. Examples including extracting masses from correlation functions or taking the chiral-continuum limit of a matrix element. We discuss the method of model averaging, a way to account for uncertainty due to model variations, from the perspective of Bayesian statistics. Statistical formulas are derived for model-averaged expectation values and for estimating the required model probability weights. In addition, we reframe the common problem of data subset selection (e.g. choice of minimum time separation for fitting a two-point correlation function) as a model selection problem and study model averaging as a universal alternative to hand tuning of fit ranges.

Primary authors

Prof. Ethan Neil (University of Colorado Boulder) Dr William Jay (Fermi National Accelerator Laboratory)

Presentation materials