Statistical methods for cosmological parameter selection and estimation

TL;DR

This paper reviews Bayesian statistical methods used in cosmological data analysis, focusing on parameter estimation, model selection, and experimental design, with implications for particle physics.

Contribution

It provides a comprehensive overview of current Bayesian statistical techniques applied to cosmological data, emphasizing their assumptions and uncertainties.

Findings

Highlights the importance of Bayesian methods in cosmology

Discusses challenges in model selection and inference

Addresses uncertainties in parameter estimation

Abstract

The estimation of cosmological parameters from precision observables is an important industry with crucial ramifications for particle physics. This article discusses the statistical methods presently used in cosmological data analysis, highlighting the main assumptions and uncertainties. The topics covered are parameter estimation, model selection, multi-model inference, and experimental design, all primarily from a Bayesian perspective.