Approximation of unsupervised Bayes learning procedures.

Makov, Udi E.. "Approximation of unsupervised Bayes learning procedures.." Trabajos de Estadística e Investigación Operativa 31.1 (1980): 69-81. <http://eudml.org/doc/40812>.

@article{Makov1980,
abstract = {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.},
author = {Makov, Udi E.},
journal = {Trabajos de Estadística e Investigación Operativa},
keywords = {Inferencia bayesiana; Algoritmos de aprendizaje; Función densidad de probabilidad; Convergencia},
language = {eng},
number = {1},
pages = {69-81},
title = {Approximation of unsupervised Bayes learning procedures.},
url = {http://eudml.org/doc/40812},
volume = {31},
year = {1980},
}

TY - JOUR
AU - Makov, Udi E.
TI - Approximation of unsupervised Bayes learning procedures.
JO - Trabajos de Estadística e Investigación Operativa
PY - 1980
VL - 31
IS - 1
SP - 69
EP - 81
AB - 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.
LA - eng
KW - Inferencia bayesiana; Algoritmos de aprendizaje; Función densidad de probabilidad; Convergencia
UR - http://eudml.org/doc/40812
ER -