| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435610 |
| 005 | | 20200228101519 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781392688472 |
| 035 | |
▼a (MiAaPQ)AAI27539758 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Sun, Ruoxi. |
| 245 | 10 |
▼a Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications. |
| 260 | |
▼a [S.l.]:
▼b Columbia University.,
▼c 2019. |
| 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 | 1 |
▼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. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0308 |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a Columbia University.
▼b Biological Sciences. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0054 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494380
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |