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Demographic Inference from Large Sam...

University of California, Berkeley.

Demographic Inference from Large Samples : = Theory and Methods.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Demographic Inference from Large Samples :/
其他題名:	Theory and Methods.
作者:	Terhorst, Jonathan.
面頁冊數:	1 online resource (100 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355034028

Demographic Inference from Large Samples : = Theory and Methods.
Terhorst, Jonathan.

Demographic Inference from Large Samples :Theory and Methods. - 1 online resource (100 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2017.

Includes bibliographical references

The emergence of next-generation sequencing has revolutionized our ability to interrogate the genome, leading to fascinating new insights into the nature of humans and many other species. At the same time it has created theoretical and computational challenges associated with the need to perform robust and efficient inference on increasingly massive genetic data sets.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355034028Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Demographic Inference from Large Samples : = Theory and Methods.
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100 1 $a Terhorst, Jonathan. $3 1195491
245 1 0 $a Demographic Inference from Large Samples : $b Theory and Methods.
264 0 $c 2017
300 $a 1 online resource (100 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Yun S. Song.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2017.
504 $a Includes bibliographical references
520 $a The emergence of next-generation sequencing has revolutionized our ability to interrogate the genome, leading to fascinating new insights into the nature of humans and many other species. At the same time it has created theoretical and computational challenges associated with the need to perform robust and efficient inference on increasingly massive genetic data sets.
520 $a In this thesis I focus on those challenges in the context of demographic inference, which estimates the past history of a population on the basis of genetic data sampled at the present. I derive the basic theoretical models which enable such inferences. I then refine them to formulate a new inference procedure for reconstructing size history using hundreds of whole genomes at a time, a significant increase over existing methods. I complement this algorithmic advance with some theoretical results on the accuracy of demographic inference as sample sizes grow large.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Berkeley. $b Statistics. $3 1180698
773 0 $t Dissertation Abstracts International $g 78-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10281900 $z click for full text (PQDT)

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