In Annual Review of Nuclear and Particle Science, vol. 59, issue 1, pp. 95-114, 2009.
http://arxiv.org/abs/0903.4210

For a PDF version of the article, click here.

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.

Table of Contents

• INTRODUCTION
• INFERENCE
• Orientation
• Bayesian inference
• Alternatives to Bayesian inference
• COSMOLOGICAL PARAMETER ESTIMATION
• Goals and methodology
• Monte Carlo methods
• Uncertainties, biases and significance
• COSMOLOGICAL MODEL SELECTION
• Model selection versus parameter estimation
• The Bayesian evidence
• Calculational methods
• Multi-model inference
• Other approaches to model selection
• FORECASTING AND EXPERIMENTAL DESIGN
• Fisher matrix approaches
• Model selection approaches
• THE END
• REFERENCES