Non-Parametric Inference in Astrophysics
Larry Wasserman (CMU; Statistics), Christopher J. Miller (CMU;, Physics), Robert C. Nichol (CMU; Physics), Chris Genovese (CMU; Statistics),, Woncheol Jang (CMU; Statistics), Andrew J. Connolly (UPitt; Physics &, Astronomy), Andrew W. Moore (CMU; CS), Jeff Schneider (CMU; CS)
TL;DR
This paper explores non-parametric methods for density estimation and regression in astrophysics, demonstrating how to compute confidence intervals for features like peaks, with applications to Cosmic Microwave Background data and a discussion on Bayesian approaches.
Contribution
It introduces techniques for non-parametric confidence interval computation in astrophysical data analysis, including practical applications and a brief overview of Bayesian methods.
Findings
Effective non-parametric confidence intervals for astrophysical features
Application to Cosmic Microwave Background data
Discussion on non-parametric Bayesian inference
Abstract
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
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Taxonomy
TopicsBayesian Methods and Mixture Models · Statistical Methods and Inference · Financial Risk and Volatility Modeling