How to Incorporate Systematic Effects into Parameter Determination

TL;DR

This paper compares pragmatic and comprehensive methods for incorporating systematic uncertainties into parameter estimation, highlighting the advantages of full likelihood and Bayesian approaches through simple and astrophysical examples.

Contribution

It introduces and compares pragmatic and full Bayesian methods for systematic effects in parameter analysis, emphasizing the benefits of the full likelihood and Bayesian approaches.

Findings

Full likelihood and Bayesian methods improve systematic incorporation.

Full Bayesian approach offers advantages over pragmatic methods.

Examples demonstrate applicability across scientific fields.

Abstract

We describe two different approaches for incorporating systematics into analyses for parameter determination in the physical sciences. We refer to these as the Pragmatic and the Full methods, with the latter coming in two variants: Full Likelihood and Fully Bayesian. By the use of a simple and readily understood example, we point out the advantage of using the Full Likelihood and Fully Bayesian approaches; a more realistic example from Astrophysics is also presented. This could be relevant for data analyses in a wide range of scientific fields, for situations where systematic effects need to be incorporated in the analysis procedure. This note is an extension of part of the talk by van Dyk at the PHYSTAT-Systematics meeting.