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Topics in large-scale statistical in...

Regier, Jeffrey Carroll.

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Topics in large-scale statistical inference.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Topics in large-scale statistical inference./
Author:	Regier, Jeffrey Carroll.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	133 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
Contained By:	Dissertation Abstracts International78-07B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10192270
ISBN:	9781369558746

Topics in large-scale statistical inference.
Regier, Jeffrey Carroll.

Topics in large-scale statistical inference. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 133 p.

Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2016.

Statistical inference may be large-scale in terms of the size of the dataset, the dimension of the data, or the amount of data needed for provably accurate inference. This dissertation presents three applications of large-scale statistical inference. Part I considers finding and characterizing stars and galaxies in images from telescopes. Part II considers figuring out who wrote what in large collection of articles, where authors often do not have unique names. Part III considers approximating a high-dimensional function based on a small number of observations, a common problem when interpreting computer experiments.

ISBN: 9781369558746Subjects--Topical Terms:

517247
Statistics.

Topics in large-scale statistical inference.
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500 $a Adviser: Jon D. McAuliffe.
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520 $a Statistical inference may be large-scale in terms of the size of the dataset, the dimension of the data, or the amount of data needed for provably accurate inference. This dissertation presents three applications of large-scale statistical inference. Part I considers finding and characterizing stars and galaxies in images from telescopes. Part II considers figuring out who wrote what in large collection of articles, where authors often do not have unique names. Part III considers approximating a high-dimensional function based on a small number of observations, a common problem when interpreting computer experiments.
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