Comparar métodos

	Base de Reglas de Creencia (BRB)	Teoría de la Evidencia Dempster-Shafer
Campo	Computación blanda	Computación blanda
Familia	Machine learning	Machine learning
Año de origen	2006	1976
Autor original	Jian-Bo Yang et al.	Arthur P. Dempster & Glenn Shafer
Tipo	Expert-system inference with belief distributions	Uncertainty calculus for combining evidence
Fuente seminal	Yang, J.-B., Liu, J., Wang, J., Sii, H.-S., & Wang, H.-W. (2006). Belief rule-base inference methodology using the evidential reasoning approach—RIMER. IEEE Transactions on Systems, Man, and Cybernetics—Part A, 36(2), 266–285.	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.
Alias	RIMER, Belief Rule-Based System, BRB System, İnanç Kural Tabanlı Çıkarım	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi
Relacionados	3	4
Resumen	Belief Rule Base (BRB), introduced by Yang et al. in 2006 under the RIMER framework, is an expert-system inference methodology that extends classical if-then rules by attaching belief degree distributions to rule consequents. It combines rule-based reasoning with the Evidential Reasoning (ER) approach, enabling the representation and propagation of uncertainty, incompleteness, and vagueness in complex decision problems across engineering, risk assessment, and management domains.	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.