Fusion of Data and Expert Judgments with Imprecise Probabilities for Decision Making

Autor:	Junxiang Tu, Zhuoning Chen, Jin Zhang, Xiaogang Wang
Rok vydání:	2008
Předmět:	Decision support system business.industry Computer science Evidential reasoning approach Decision tree Machine learning computer.software_genre Bayesian statistics Influence diagram Artificial intelligence Data mining business Decision model computer Optimal decision Decision analysis
Zdroj:	Intelligent Robotics and Applications ISBN: 9783540885160 ICIRA (2)
DOI:	10.1007/978-3-540-88518-4_87
Popis:	Bayesian approaches to decision analysis are geared towards determining the optimal alternative by combining expert knowledge and data in small sample situations. However, Traditional Bayesian decision models assume that all probabilities are precise. In practical applications the probabilities of the states of nature are often under uncertainty due to the imprecision in the experts' judgments. In this paper, we present a decision support model that uses interval-valued probabilities to represent experts' uncertain beliefs. The model performs reasoning and decision making by integrating expert uncertain information and historical statistical data for Bayesian analysis. An example concerning risk assessment is given to demonstrate the applicability of the model in a real-world domain.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b7b73ff1137c2443861d4029dc3bd0d0 https://doi.org/10.1007/978-3-540-88518-4_87 Zobrazit plný text záznamu