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Modern Statistical/Machine Learning ...

Sun, Ruoxi.

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Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications./
Author:	Sun, Ruoxi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	113 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27539758
ISBN:	9781392688472

Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications.
Sun, Ruoxi.

Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 113 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--Columbia University, 2019.

This item must not be sold to any third party vendors.

Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections.

ISBN: 9781392688472Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Bayesian statistics

Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications.
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500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 $a Includes supplementary digital materials.
500 $a Advisor: Paninski, Liam.
502 $a Thesis (Ph.D.)--Columbia University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections.
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653 $a Bayesian statistics
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653 $a Computational biology
653 $a Computational neuroscience
653 $a Machine learning
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