MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02455cam a2200205 i 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20160322165207.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
120522s2013 enka b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781107026483 (Hardback) |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.2 |
| Item number |
KLE |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kleinberg, Samantha, |
| 9 (RLIN) |
2193 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Causality, probability, and time / |
| Statement of responsibility, etc. |
Samantha Kleinberg, Stevens Institute of Technology, Hoboken, New Jersey. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
Cambridge: |
| Name of publisher, distributor, etc. |
Cambridge University Press, |
| Date of publication, distribution, etc. |
2013. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
vii, 259 pages : |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"This book presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships. The method's feasibility and success is demonstrated through theoretical and experimental case studies"-- |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Whether we want to know the cause of a stock's price movements (in order to trade on this information), the key phrases that can alter public opinion of a candidate (in order to optimize a politician's speeches) or which genes work together to regulate a disease causing process (in order to intervene and disrupt it), many goals center on finding and using causes. Causes tell us not only that two phenomena are related, but how they are related. They allow us to make robust predictions about the future, explain the relationship between and occurrence of events, and develop effective policies for intervention. While predictions are often made successfully on the basis of associations alone, these relationships can be unstable. If we do not know why the resulting models work, we cannot predict when they will stop working. Lung cancer rates in an area may be correlated with match sales if many smokers use matches to light their cigarettes, but match sales may also be influenced by blackouts and seasonal trends (with many purchases around holidays or in winter). A spike in match sales due to a blackout will not result in the predicted spike in lung cancer rates, but without knowledge of the underlying causes we would not be able to anticipate that failure. Models based on associations can also lead to redundancies, since multiple effects of the true cause may be included as they are correlated with its occurrence. In applications to the biomedical domain, this can result in unnecessary diagnostic tests that may be invasive and expensive"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computational complexity. |
| 9 (RLIN) |
400 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
COMPUTERS / Natural Language Processing. |
| 9 (RLIN) |
2194 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
