國立虎尾科技大學 |

An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States./
作者:	Farouni, Tarek.
面頁冊數:	1 online resource (128 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
標題:	Quantitative psychology. -
電子資源:	click for full text (PQDT)
ISBN:	9780355317329

An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States.
Farouni, Tarek.

An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States. - 1 online resource (128 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--The Ohio State University, 2017.

Includes bibliographical references

The following dissertation consists of two parts. The first part presents an overview of latent variable models from a probabilistic perspective. The main goal of the overview is to give a birds-eye view of the topographic structure of the space of latent variable models in light of recent developments in statistics and machine learning that show how seemingly unrelated models and methods are in fact intimately related to each other.

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

Mode of access: World Wide Web

ISBN: 9780355317329Subjects--Topical Terms:

1182802
Quantitative psychology.
Index Terms--Genre/Form:

554714
Electronic books.

An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States.
LDR:02721ntm a2200349K 4500 001 914384
005 20180703084809.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355317329
035 $a (MiAaPQ)AAI10665946
035 $a (MiAaPQ)OhioLINK:osu1492189894812539
035 $a AAI10665946
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Farouni, Tarek. $3 1187625
245 1 3 $a An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States.
264 0 $c 2017
300 $a 1 online resource (128 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Robert Cudeck.
502 $a Thesis (Ph.D.)--The Ohio State University, 2017.
504 $a Includes bibliographical references
520 $a The following dissertation consists of two parts. The first part presents an overview of latent variable models from a probabilistic perspective. The main goal of the overview is to give a birds-eye view of the topographic structure of the space of latent variable models in light of recent developments in statistics and machine learning that show how seemingly unrelated models and methods are in fact intimately related to each other.
520 $a In the second part of the dissertation, we apply a Deep Latent Gaussian Model (DLGM) to high-dimensional, high-throughput functional epigenomics datasets with the goal of learning a latent representation of functional regions of the genome, both across DNA sequence and across cell-types. In the latter half of the dissertation, we first demonstrate that the trained generative model is able to learn a compressed two-dimensional latent representation of the data. We then show how the learned latent space is able to capture the most salient patterns of dependencies in the observations such that synthetic samples simulated from the latent manifold are able to reconstruct the same patterns of dependencies we observe in data samples. Lastly, we provide a biological interpretation of the learned latent manifold in terms of a continuous histone code and explain both the relevance and significance of the proposed generative approach to the problem of identifying functional regulatory regions from epigenomic marks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Quantitative psychology. $3 1182802
650 4 $a Bioinformatics. $3 583857
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0632
690 $a 0715
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The Ohio State University. $b Psychology. $3 1180990
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10665946 $z click for full text (PQDT)