Enfoques bayesianos para la toma de decisiones bajo incertidumbre en sistemas inteligentes: una revisión multidisciplinaria
Palabras clave:
Inferencia bayesiana, toma de decisiones, redes neuronales bayesianas, ingeniería de procesos, incertidumbre epistémica, sistemas inteligentesResumen
Este artículo de revisión aborda la evolución del razonamiento en sistemas inteligentes, desde los enfoques deterministas clásicos hacia los paradigmas bayesianos probabilísticos. Se analizan los fundamentos de la toma de decisiones bajo incertidumbre, haciendo énfasis en la distinción entre incertidumbre aleatoria y epistémica, así como en la integración del teorema de Bayes con la teoría de la utilidad. Se exploran modelos avanzados como las redes bayesianas, procesos gaussianos y redes neuronales bayesianas (BNN), destacando técnicas de aproximación práctica como el Monte Carlo Dropout. La revisión culmina con un análisis de aplicaciones críticas en ingeniería química y visión por computadora, consolidando el enfoque bayesiano como un marco robusto para la cuantificación de la incertidumbre en entornos de alto riesgo.Referencias
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