Salīdzināt metodes

	Dempster-Šaferas teorija par pierādījumiem	Bayes' tīkls
Nozare	Mīkstā skaitļošana	Bajesa metodes
Saime	Machine learning	Bayesian methods
Izcelsmes gads	1976	1988
Autors	Arthur P. Dempster & Glenn Shafer	Judea Pearl
Tips	Uncertainty calculus for combining evidence	Probabilistic graphical model
Pirmavots	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
Citi nosaukumi	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	Bayes network, belief network, probabilistic graphical model, directed graphical model
Saistītās	4	4
Kopsavilkums	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.