| 000 | 00921camuuu200265 a 4500 | |
| 001 | 000001023408 | |
| 005 | 19990122164010.0 | |
| 008 | 960718s1997 nyua b 001 0 eng | |
| 010 | ▼a 96033161 //r97 | |
| 020 | ▼a 0387948589 (New York : acid-free paper) : ▼c $59.95 | |
| 040 | ▼a DLC ▼c DLC ▼2 44002 | |
| 049 | 0 | ▼l 151058837 |
| 050 | 0 0 | ▼a QA76.76.E95 ▼b C378 1997 |
| 082 | 0 0 | ▼a 006.3/3 ▼2 20 |
| 090 | ▼a 006.33 ▼b C352e | |
| 100 | 1 | ▼a Castillo, Enrique, ▼d 1946- |
| 245 | 1 0 | ▼a Expert systems and probabilistic network models / ▼c Enrique Castillo, Jose' Manuel Gutie'rrez, Ali S. Hadi. |
| 260 | ▼a New York : ▼b Springer, ▼c c1997. | |
| 300 | ▼a xiv, 605 p. : ▼b ill. ; ▼c 24 cm. | |
| 440 | 0 | ▼a Monographs in computer science. |
| 504 | ▼a Includes bibliographical references (p. [581]-596) and index. | |
| 650 | 0 | ▼a Expert systems (Computer science). |
| 650 | 0 | ▼a Probabilities. |
| 700 | 1 | ▼a Gutie'rrez, Jose' Manuel. |
| 700 | 1 | ▼a Hadi, Ali S. |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.33 C352e | 등록번호 121104872 (1회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 2 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.33 C352e | 등록번호 151058837 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.33 C352e | 등록번호 121104872 (1회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.33 C352e | 등록번호 151058837 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Expert systems and uncertainty in artificial intelligence have seen a great surge of research activity during the last decade. This book provides a clear and up-to-date account of the research progress in these areas.The authors begin with a survey of rule-based expert systems, which are mainly applicable to deterministic situations. Since most practical applications involve some degree of uncertainty, the authors then introduce probabilistic expert systems to deal with this element of uncertainty. They build on this foundation by showing how coherent expert systems are constructed and how probabilistic models such as Bayesian and Markov networks are developed. Subsequent chapters discuss how knowledge is updated by using both exact and approximate propagation methods. Other subjects such as symbolic propagation, sensitivity analysis, and learning are also presented. The book concludes with a chapter that applies the methods presented in the book to some case studies of real-life applications.The concepts, ideas, and algorithms are illustrated by more than 150 examples and more than 250 graphs with the aid of computer programs developed by the authors. These programs can be obtained from a World Wide Web site (see the address in the preface). The book also includes end-of-chapter exercises and an extensive bibliography.This book is intended for advanced undergraduate and graduate students, and for research workers and professionals from a variety of fields, including computer science, applied mathematics, statistics, engineering, medicine, business, economics, and social sciences. No previous knowledge of expert systems is assumed. Readers are assumed to have some background in probability and statistics.
정보제공 :
목차
From the contents: Rule-based expert systems.- Probabilistic expert systems.- Some concepts of graphs.- Building probabalistic models.- Graphically specified models.- Extending graphically specified models.- Exact propagation in probabilistic network models.- Approximate propagation methods.- Symbolic propagation of evidence.- Learning Bayesian models.- Case studies.
정보제공 :