UNCERTAINTY QUANTIFICATION IN DYNAMIC IMAGE RECONSTRUCTION WITH APPLICATIONS TO CRYO-EM

Tze Leung Lai; Shao Hsuan Wang; Szu Chi Chung; Wei Hau Chang; I. Ping Tu

doi:10.5705/ss.202021.0419

UNCERTAINTY QUANTIFICATION IN DYNAMIC IMAGE RECONSTRUCTION WITH APPLICATIONS TO CRYO-EM

Tze Leung Lai, Shao Hsuan Wang, Szu Chi Chung, Wei Hau Chang, I. Ping Tu

統計研究所

研究成果: 雜誌貢獻 › 期刊論文 › 同行評審

摘要

Here, we propose combining empirical Bayes modeling with recent advances in Markov chain Monte Carlo filters for hidden Markov models. In doing so, we address long-standing problems in the reconstruction of 3D images, with uncertainty quantification, from noisy 2D pixels in cryogenic electron microscopy and other applications, such as brain network development in infants.

原文	???core.languages.en_GB???
頁（從 - 到）	1771-1788
頁數	18
期刊	Statistica Sinica
卷	33
DOIs	https://doi.org/10.5705/ss.202021.0419
出版狀態	已出版 - 5月 2023

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10.5705/ss.202021.0419

交叉數據矩陣型PCA：理論與應用(3/3)
Wang, S.-H. (PI)
1/10/22 → 31/10/23
研究計畫: Research

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abstract = "Here, we propose combining empirical Bayes modeling with recent advances in Markov chain Monte Carlo filters for hidden Markov models. In doing so, we address long-standing problems in the reconstruction of 3D images, with uncertainty quantification, from noisy 2D pixels in cryogenic electron microscopy and other applications, such as brain network development in infants.",

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author = "Lai, {Tze Leung} and Wang, {Shao Hsuan} and Chung, {Szu Chi} and Chang, {Wei Hau} and Tu, {I. Ping}",

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AU - Lai, Tze Leung

AU - Wang, Shao Hsuan

AU - Chung, Szu Chi

AU - Chang, Wei Hau

AU - Tu, I. Ping

PY - 2023/5

Y1 - 2023/5

N2 - Here, we propose combining empirical Bayes modeling with recent advances in Markov chain Monte Carlo filters for hidden Markov models. In doing so, we address long-standing problems in the reconstruction of 3D images, with uncertainty quantification, from noisy 2D pixels in cryogenic electron microscopy and other applications, such as brain network development in infants.

AB - Here, we propose combining empirical Bayes modeling with recent advances in Markov chain Monte Carlo filters for hidden Markov models. In doing so, we address long-standing problems in the reconstruction of 3D images, with uncertainty quantification, from noisy 2D pixels in cryogenic electron microscopy and other applications, such as brain network development in infants.

KW - Change-points

KW - Markov chain Monte Carlo

KW - cryogenic electron microscopy

KW - empirical Bayes

KW - hidden Markov models

KW - particle filters

KW - stem cells

KW - uncertainty quantification

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M3 - 期刊論文

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JO - Statistica Sinica

JF - Statistica Sinica

ER -

UNCERTAINTY QUANTIFICATION IN DYNAMIC IMAGE RECONSTRUCTION WITH APPLICATIONS TO CRYO-EM

摘要

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專案

交叉數據矩陣型PCA：理論與應用(3/3)

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