A Nested Sampling Algorithm for Cosmological Model Selection | Zendy

Pia Mukherjee | Zendy; David Parkinson | Zendy; Andrew R. Liddle | Zendy

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A Nested Sampling Algorithm for Cosmological Model Selection

Author(s) -

Pia Mukherjee,

David Parkinson,

Andrew R. Liddle

Publication year - 2006

Publication title -

the astrophysical journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.376

H-Index - 489

eISSN - 1538-4357

pISSN - 0004-637X

DOI - 10.1086/501068

Subject(s) - generality , model selection , selection (genetic algorithm) , sampling (signal processing) , computer science , range (aeronautics) , bayesian probability , algorithm , nested set model , bayesian inference , econometrics , statistics , machine learning , artificial intelligence , data mining , mathematics , filter (signal processing) , relational database , computer vision , psychology , materials science , composite material , psychotherapist

The abundance of new cosmological data becoming available means that a widerrange of cosmological models are testable than ever before. However, animportant distinction must be made between parameter fitting and modelselection. While parameter fitting simply determines how well a model fits thedata, model selection statistics, such as the Bayesian Evidence, are nownecessary to choose between these different models, and in particular to assessthe need for new parameters. We implement a new evidence algorithm known asnested sampling, which combines accuracy, generality of application andcomputational feasibility, and apply it to some cosmological datasets andmodels. We find that a five-parameter model with Harrison-Zel'dovich initialspectrum is currently preferred.Comment: 4 pages, 3 figures. Minor updates to match version accepted by Astrophys J Letter

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