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

Graduate Institute of Statistics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1771-1788
Number of pages	18
Journal	Statistica Sinica
Volume	33
DOIs	https://doi.org/10.5705/ss.202021.0419
State	Published - May 2023

Keywords

Change-points
Markov chain Monte Carlo
cryogenic electron microscopy
empirical Bayes
hidden Markov models
particle filters
stem cells
uncertainty quantification

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10.5705/ss.202021.0419

Cross Data Matrix-Based PCA: Theory and Applications(3/3)
Wang, S.-H. (PI)
1/10/22 → 31/10/23
Project: Research

Cite this

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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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EP - 1788

JO - Statistica Sinica

JF - Statistica Sinica

ER -

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

Abstract

Keywords

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Projects

Cross Data Matrix-Based PCA: Theory and Applications(3/3)

Cite this