2021 Student Research Conference:
34th Annual Student Research Conference

Bayesian Inference and Cosmological Data Analysis


Andrew D. Gentry
Prof. Timothy Wiser, Faculty Mentor

Bayesian inference is a statistical method that is becoming increasingly popular for use in astrophysical and cosmological parameter estimation and model comparison. The key advantage of Bayesian inference is the ability to account for prior information when analyzing statistical error. Bayes’ theorem provides a quantitative formula for updating a hypothesis based on experiment. Bayesian inference is more useful than traditional “frequentist” methods when complicated cosmological theories combined with large, noisy datasets make it difficult to create a consistent way to analyze data. We present the statistical framework of Bayesian inference, especially focusing on its use in parameter estimation. Bayesian inference often necessitates the use of computational techniques. We discuss Markov Chain Monte Carlo (MCMC), the primary numerical tool used in modern cosmology. Several of the most popular MCMC algorithms will be reviewed, and an illustrative example using data from the cosmic microwave background will be presented.

Keywords: cosmology, bayes , bayes theorem, cosmic microwave background, Monte Carlo

Topic(s):Physics
Astronomy
Statistics

Presentation Type: Face-to-Face Oral Presentation

Session: 401-2
Location: SUB GEO
Time: 3:45

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