方法对比

	证据的Dempster-Shafer理论	贝叶斯网络
领域	软计算	贝叶斯
方法族	Machine learning	Bayesian methods
起源年份	1976	1988
提出者	Arthur P. Dempster & Glenn Shafer	Judea Pearl
类型	Uncertainty calculus for combining evidence	Probabilistic graphical model
开创性文献	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797
别名	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	Bayes network, belief network, probabilistic graphical model, directed graphical model
相关	4	4
摘要	Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.