Approximation of unsupervised Bayes learning procedures.

Título inglés	Approximation of unsupervised Bayes learning procedures.
Título español	Aproximación a los procesos de aprendizaje bayesianos no supervisados.
Autor/es	Makov, Udi E.
Organización	Dep. Math. Chelsea Coll., Londres, Reino Unido
Revista	0041-0241
Publicación	1980, 31 (1): 69-81, 35 Ref.
Tipo de documento	articulo
Idioma	Inglés
Resumen inglés	Computational constraints often limit the practical applicability of coherent Bayes solutions to unsupervised sequential learning problems. These problems arise when attemps are made to learn about parameters on the basis of unclassified observations., each stemming from any one of k cases (k ≥ 2). In this paper, the difficulties of the Bayes process will be discussed and existing approximate learning procedures will be reviewed for broad types of problems involving mixtures of probability density. In particular a quasi-Bayes approximate learning procedure will be motivated and defined and its convergence properties will be reported for several special cases.
Clasificación UNESCO	120913 ; 120304
Palabras clave español	Inferencia bayesiana ; Algoritmos de aprendizaje ; Función densidad de probabilidad ; Convergencia
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