1. NCCU Academic Hub
  2. 學術產出
  3. College of Informatics
  4. Proceedings
  5. Item

學術產出-Proceedings

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

  • No doi shows Citation Infomation
題名 Advances in applying stochastic-dominance relationships to bounding probability distributions in Bayesian networks
作者 劉昭麟
Liu, Chao-Lin
貢獻者 IASTED
國立政治大學資訊科學系
關鍵詞 stochastic-dominance relationships;bounding probability distributions;Bayesian networks
日期 2002-09
上傳時間 27-May-2010 16:48:38 (UTC+8)
摘要 Bounds of probability distributions are useful for many reasoning tasks, including resolving the qualitative ambi- guities in qualitative probabilistic networks and search- ing the best path in stochastic transportation networks. This paper investigates a subclass of the state-space ab- straction methods that are designed to approximately evaluate Bayesian networks. Taking advantage of par- ticular stochastic-dominance relationships among ran- dom variables, these special methods aggregate states of random variables to obtain bounds of probability dis- tributions at much reduced computational costs, thereby achieving high responsiveness of the overall system. The existing methods demonstrate two drawbacks, however. The strict reliance on the particular stochastic- dominance relationships confines their applicability. Also, designed for general Bayesian networks, these methods might not achieve their best performance in spe- cial domains, such as fastest-path planning problems. The author elaborates on these problems, and offers ex- tensions to improve the existing approximation tech- niques.
關聯 Proceedings of the IASTED International Conference on Artificial and Computational Intelligence 2002
資料類型 conference
dc.contributor IASTEDen_US
dc.contributor 國立政治大學資訊科學系en_US
dc.creator (作者) 劉昭麟zh_TW
dc.creator (作者) Liu, Chao-Lin-
dc.date (日期) 2002-09en_US
dc.date.accessioned 27-May-2010 16:48:38 (UTC+8)-
dc.date.available 27-May-2010 16:48:38 (UTC+8)-
dc.date.issued (上傳時間) 27-May-2010 16:48:38 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/39690-
dc.description.abstract (摘要) Bounds of probability distributions are useful for many reasoning tasks, including resolving the qualitative ambi- guities in qualitative probabilistic networks and search- ing the best path in stochastic transportation networks. This paper investigates a subclass of the state-space ab- straction methods that are designed to approximately evaluate Bayesian networks. Taking advantage of par- ticular stochastic-dominance relationships among ran- dom variables, these special methods aggregate states of random variables to obtain bounds of probability dis- tributions at much reduced computational costs, thereby achieving high responsiveness of the overall system. The existing methods demonstrate two drawbacks, however. The strict reliance on the particular stochastic- dominance relationships confines their applicability. Also, designed for general Bayesian networks, these methods might not achieve their best performance in spe- cial domains, such as fastest-path planning problems. The author elaborates on these problems, and offers ex- tensions to improve the existing approximation tech- niques.-
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Proceedings of the IASTED International Conference on Artificial and Computational Intelligence 2002en_US
dc.subject (關鍵詞) stochastic-dominance relationships;bounding probability distributions;Bayesian networksen_US
dc.title (題名) Advances in applying stochastic-dominance relationships to bounding probability distributions in Bayesian networksen_US
dc.type (資料類型) conferenceen