Jump to ContentJump to Main Navigation
Statistics, Data Mining, and Machine Learning in AstronomyA Practical Python Guide for the Analysis of Survey Data$
Users without a subscription are not able to see the full content.

Željko Ivezic, Andrew J. Connolly, Jacob T VanderPlas, and Alexander Gray

Print publication date: 2014

Print ISBN-13: 9780691151687

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691151687.001.0001

Show Summary Details
Page of

PRINTED FROM PRINCETON SCHOLARSHIP ONLINE (www.princeton.universitypressscholarship.com). (c) Copyright Princeton University Press, 2017. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in HSO for personal use (for details see http://www.universitypressscholarship.com/page/privacy-policy).date: 21 April 2018

Searching for Structure in Point Data

Searching for Structure in Point Data

Chapter:
(p.249) 6 Searching for Structure in Point Data
Source:
Statistics, Data Mining, and Machine Learning in Astronomy
Author(s):

Željko Ivezi

Andrew J. Connolly

Jacob T. VanderPlas

Alexander Gray

Željko Ivezi

Andrew J. Connolly

Jacob T. VanderPlas

Alexander Gray

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691151687.003.0006

Inferring the probability density function (pdf) from a sample of data is known as density estimation. The same methodology is often called data smoothing. Density estimation in the one-dimensional case has been discussed in the previous chapters. This chapter extends it to multidimensional cases. Density estimation is one of the most critical components of extracting knowledge from data. For example, given a pdf estimated from point data, we can generate simulated distributions of data and compare them against observations. If we can identify regions of low probability within the pdf, we have a mechanism for the detection of unusual or anomalous sources. If our point data can be separated into subsamples using provided class labels, we can estimate the pdf for each subsample and use the resulting set of pdfs to classify new points: the probability that a new point belongs to each subsample/class is proportional to the pdf of each class evaluated at the position of the point.

Keywords:   density estimation, probability density functions, statistical inference, data smoothing

Princeton Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us.