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A nested sampling algorithm for cosmological model selection
journal contributionposted on 2023-06-07, 19:41 authored by Pia Mukherjee, David Parkinson, Andrew R Liddle
The abundance of new cosmological data becoming available means that a wider range of cosmological models are testable than ever before. However, an important distinction must be made between parameter fitting and model selection. While parameter fitting simply determines how well a model fits the data, model selection statistics, such as the Bayesian Evidence, are now necessary to choose between these different models, and in particular to assess the need for new parameters. We implement a new evidence algorithm known as nested sampling, which combines accuracy, generality of application and computational feasibility, and apply it to some cosmological datasets and models. We find that a five-parameter model with Harrison-Zel'dovich initial spectrum is currently preferred.
- Published version
JournalAstrophysical Journal Letters
Department affiliated with
- Physics and Astronomy Publications
NotesThis was the first efficient and accurate implementation of model selection in cosmology. It has generated significant interest in the community. I contributed fully regarding implementation and the writing of code. The success of this effort initiated other papers on further applications of interest.
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