東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Modern Statistical/Machine Learning ...

Sun, Ruoxi.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications./
作者:	Sun, Ruoxi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	113 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
標題:	Biostatistics. -
電子資源:	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.
LDR:01917nmm a2200385 4500 001 2272847
005 20201105110240.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781392688472
035 $a (MiAaPQ)AAI27539758
035 $a AAI27539758
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sun, Ruoxi. $3 3550271
245 1 0 $a Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 113 p.
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.
590 $a School code: 0054.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
650 4 $a Computer science. $3 523869
653 $a Bayesian statistics
653 $a Bio-imaging
653 $a Computational biology
653 $a Computational neuroscience
653 $a Machine learning
690 $a 0308
690 $a 0463
690 $a 0984
710 2 $a Columbia University. $b Biological Sciences. $3 3191709
773 0 $t Dissertations Abstracts International $g 81-04B.
790 $a 0054
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27539758