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Probability distribution construction via deep learning

The pursuit of estimating probability distributions of complex data is an ongoing challenge. Existing traditional methods impose a ceiling to the true resemblance of the targeted data distribution, due to their assumptions on the shape of the targeted data distribution. Recently, generative models h...

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Main Author: Tan, Hannah E-Ling
Format: Final Year Project / Dissertation / Thesis
Published: 2023
Subjects:
QA Mathematics
Online Access:http://eprints.utar.edu.my/6152/1/HANNAH_TAN_E%2DLING%2D2005143.pdf
http://eprints.utar.edu.my/6152/
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spelling my-utar-eprints.61522023-12-12T08:26:32Z Probability distribution construction via deep learning Tan, Hannah E-Ling QA Mathematics The pursuit of estimating probability distributions of complex data is an ongoing challenge. Existing traditional methods impose a ceiling to the true resemblance of the targeted data distribution, due to their assumptions on the shape of the targeted data distribution. Recently, generative models have garnered substantial attention for its ability to replicate high-resolution images, thereby learning the distribution of high-complexity data. Inspired by this paradigmatic approach to learn a distribution without relying on an assumption about the shape of the target data distribution, this project explores the bridging of Deep Learning and Statistics within the area of distribution generation methods. This paper provides the overall context of the research problem in Chapter 1, elaborates on existing literature and related works in Chapter 2, discusses the methodology and execution plan of this project in Chapter 3, mentions the results from what was executed in Chapter 4 and lastly concludes in Chapter 5. 2023 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6152/1/HANNAH_TAN_E%2DLING%2D2005143.pdf Tan, Hannah E-Ling (2023) Probability distribution construction via deep learning. Final Year Project, UTAR. http://eprints.utar.edu.my/6152/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic QA Mathematics
spellingShingle QA Mathematics
Tan, Hannah E-Ling
Probability distribution construction via deep learning
description The pursuit of estimating probability distributions of complex data is an ongoing challenge. Existing traditional methods impose a ceiling to the true resemblance of the targeted data distribution, due to their assumptions on the shape of the targeted data distribution. Recently, generative models have garnered substantial attention for its ability to replicate high-resolution images, thereby learning the distribution of high-complexity data. Inspired by this paradigmatic approach to learn a distribution without relying on an assumption about the shape of the target data distribution, this project explores the bridging of Deep Learning and Statistics within the area of distribution generation methods. This paper provides the overall context of the research problem in Chapter 1, elaborates on existing literature and related works in Chapter 2, discusses the methodology and execution plan of this project in Chapter 3, mentions the results from what was executed in Chapter 4 and lastly concludes in Chapter 5.
format Final Year Project / Dissertation / Thesis
author Tan, Hannah E-Ling
author_facet Tan, Hannah E-Ling
author_sort Tan, Hannah E-Ling
title Probability distribution construction via deep learning
title_short Probability distribution construction via deep learning
title_full Probability distribution construction via deep learning
title_fullStr Probability distribution construction via deep learning
title_full_unstemmed Probability distribution construction via deep learning
title_sort probability distribution construction via deep learning
publishDate 2023
url http://eprints.utar.edu.my/6152/1/HANNAH_TAN_E%2DLING%2D2005143.pdf
http://eprints.utar.edu.my/6152/
_version_ 1787140958309056512
score 13.211869

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