The Squealer: Sensification of model exploration and model misfit
Andrew Gelman, Andrew H. Jaffe, Eliot Carlson, Philip Greengard
physics.data-an, astro-ph.CO, stat.ME
Submitted: 2026-06-29
Comments: 18 pages, 11 figures. Submitted to the Journal of Computational and Graphical Statistics
Code: https://github.com/elc45/squealer-prototype
License: http://creativecommons.org/licenses/by/4.0/
The gist: We introduce a method for visual and auditory feedback when exploring the fit of a model to data.
Terminology
Abstract
We introduce a method for visual and auditory feedback when exploring the fit of a model to data. Starting with a best-fit curve fit to data, the user can drag the curve to a new position and the computer will emit a squeal, becoming louder and more unpleasant as the discrepancy between curve and data increases. We demonstrate with four examples: a two-parameter curve fit to golf putting data, a four-parameter curve fit to dilution assays, a fit to cosmological data sensitive to the parameters of the Big Bang model, and a nonparametric Gaussian process fit to temperature readings.
Sources
- A unified pseudo-$C_\ell$ framework
- Radical Compression of Cosmic Microwave Background Data
- MASTER of the CMB Anisotropy Power Spectrum: A Fast Method for Statistical Analysis of Large and Complex CMB Data Sets
- The Physics of Microwave Background Anisotropies
- Planck 2018 results. V. CMB power spectra and likelihoods
- Planck 2018 results. VI. Cosmological parameters
- Delivering data differently
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