Practical Approaches to Causal Relationship Exploration (Record no. 52389)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03082nam a22005175i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-14433-7 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200420221247.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 150302s2015 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319144337 |
| -- | 978-3-319-14433-7 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Li, Jiuyong. |
| 245 10 - TITLE STATEMENT | |
| Title | Practical Approaches to Causal Relationship Exploration |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | X, 80 p. 55 illus. |
| 490 1# - SERIES STATEMENT | |
| Series statement | SpringerBriefs in Electrical and Computer Engineering, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Local causal discovery with a simple PC algorithm -- A local causal discovery algorithm for high dimensional data -- Causal rule discovery with partial association test -- Causal rule discovery with cohort studies -- Experimental comparison and discussions. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Liu, Lin. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Le, Thuc Duy. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-14433-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2015. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2191-8112 |
| 912 ## - | |
| -- | ZDB-2-SCS |
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