| LDR | | 01869nam u200409 4500 |
| 001 | | 000000420129 |
| 005 | | 20190215164228 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438016101 |
| 035 | |
▼a (MiAaPQ)AAI10826624 |
| 035 | |
▼a (MiAaPQ)ucla:16857 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 020 |
| 100 | 1 |
▼a Matiasz, Nicholas John. |
| 245 | 10 |
▼a Planning Experiments with Causal Graphs. |
| 260 | |
▼a [S.l.]:
▼b University of California, Los Angeles.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 123 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: A. |
| 500 | |
▼a Advisers: Alex A T Bui |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Los Angeles, 2018. |
| 520 | |
▼a Scientists aim to design experiments and analyze evidence to obtain maximum knowledge. Although scientists have many statistical methods to guide how they analyze evidence, they have relatively few methods to quantify the convergence of evidence |
| 520 | |
▼a This dissertation shows how scientific results can be merged to yield new inferences by determining whether the results are consistent with various causal structures. Also presented is a Bayesian model of scientific consensus building, based on |
| 590 | |
▼a School code: 0031. |
| 650 | 4 |
▼a Information science. |
| 650 | 4 |
▼a Biology. |
| 690 | |
▼a 0723 |
| 690 | |
▼a 0306 |
| 710 | 20 |
▼a University of California, Los Angeles.
▼b Bioengineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10A(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0031 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998913
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |