Aug 4 – 7, 2020
Asia/Tokyo timezone

Bayesian Model Averaging

Aug 5, 2020, 4:40 PM


Algorithms, machines, and code development Algorithms, machines, and code development


William Jay (*) (Fermilab)


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 author


Presentation materials