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Data analysis recipes: Fitting a model to data

14 Pith papers cite this work. Polarity classification is still indexing.

14 Pith papers citing it
abstract

We go through the many considerations involved in fitting a model to data, using as an example the fit of a straight line to a set of points in a two-dimensional plane. Standard weighted least-squares fitting is only appropriate when there is a dimension along which the data points have negligible uncertainties, and another along which all the uncertainties can be described by Gaussians of known variance; these conditions are rarely met in practice. We consider cases of general, heterogeneous, and arbitrarily covariant two-dimensional uncertainties, and situations in which there are bad data (large outliers), unknown uncertainties, and unknown but expected intrinsic scatter in the linear relationship being fit. Above all we emphasize the importance of having a "generative model" for the data, even an approximate one. Once there is a generative model, the subsequent fitting is non-arbitrary because the model permits direct computation of the likelihood of the parameters or the posterior probability distribution. Construction of a posterior probability distribution is indispensible if there are "nuisance parameters" to marginalize away.

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representative citing papers

AMIGO: a Data-Driven Calibration of the JWST Interferometer

astro-ph.IM · 2025-10-10 · unverdicted · novelty 7.0

AMIGO is an end-to-end differentiable forward model of JWST AMI that corrects detector systematics to recover high-precision astrometry and detect close high-contrast companions.

emcee: The MCMC Hammer

astro-ph.IM · 2012-02-16 · accept · novelty 7.0

emcee delivers a stable Python implementation of the affine-invariant ensemble MCMC algorithm that requires minimal hand-tuning and supports easy parallelization.

Probabilistic Analysis of Event-Mode Experimental Data

physics.ins-det · 2025-02-25 · unverdicted · novelty 6.0

Direct probabilistic modeling of raw event-mode scattering data claims greater efficiency and lower systematic error than histogram-plus-least-squares methods.

A 3D Dust Map Based on Gaia, Pan-STARRS 1 and 2MASS

astro-ph.GA · 2019-05-07 · unverdicted · novelty 6.0

A new 3D dust reddening map with finer distance resolution, a spatial correlation prior, and Gaia-based distances covering the sky north of -30 degrees declination out to several kiloparsecs.

Stellar Population Inference with Prospector

astro-ph.GA · 2020-12-02 · unverdicted · novelty 4.0

Prospector is a flexible code for Bayesian inference of stellar population parameters from multi-wavelength photometry and spectroscopy via forward modeling and posterior sampling.

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