Collaborative Nested Sampling: Big Data versus Complex Physical Models

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dc.contributor.authorBuchner, Johannes
dc.date.accessioned2025-01-23T21:11:09Z
dc.date.available2025-01-23T21:11:09Z
dc.date.issued2019
dc.description.abstractThe data torrent unleashed by current and upcoming astronomical surveys demands scalable analysis methods. Many machine learning approaches scale well, but separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is often an afterthought. Classic forward-folding analyses with Markov chain Monte Carlo or nested sampling enable parameter estimation and model comparison, even for complex and slow-to-evaluate physical models. However, these approaches require independent runs for each data set, implying an unfeasible number of model evaluations in the Big Data regime. Here I present a new algorithm, collaborative nested sampling, for deriving parameter probability distributions for each observation. Importantly, the number of physical model evaluations scales sub-linearly with the number of data sets, and no assumptions about homogeneous errors, Gaussianity, the form of the model, or heterogeneity/completeness of the observations need to be made. Collaborative nested sampling has immediate applications in speeding up analyses of large surveys, integral-field-unit observations, and Monte Carlo simulations.
dc.fuente.origenWOS
dc.identifier.doi10.1088/1538-3873/aae7fc
dc.identifier.eissn1538-3873
dc.identifier.issn0004-6280
dc.identifier.urihttps://doi.org/10.1088/1538-3873/aae7fc
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/100888
dc.identifier.wosidWOS:000484125300003
dc.issue.numero1004
dc.language.isoen
dc.revistaPublications of the astronomical society of the pacific
dc.rightsacceso restringido
dc.subjectmethods: data analysis
dc.subjectmethods: statistical
dc.subjectsurveys
dc.titleCollaborative Nested Sampling: Big Data versus Complex Physical Models
dc.typeartículo
dc.volumen131
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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