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An Overview of Probabilistic Latent ...

Farouni, Tarek.

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

紀錄類型:	書目-電子資源 : 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.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	128 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Quantitative psychology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10665946
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. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 128 p.

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

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

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.

ISBN: 9780355317329Subjects--Topical Terms:

2144748
Quantitative psychology.

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