@Article{2019.100000068, AUTHOR = {He Ni, Yongqiao Wang, Buyun Xu}, TITLE = {Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation}, JOURNAL = {Intelligent Automation \& Soft Computing}, VOLUME = {26}, YEAR = {2020}, NUMBER = {2}, PAGES = {227--236}, URL = {http://www.techscience.com/iasc/v26n2/39940}, ISSN = {2326-005X}, ABSTRACT = {The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such as Mixture Gaussian Autoregressive Model, Self-Organizing Mixture Map, and have some favoured attributes on its robust convergence and good generalization. The experimental results on both artificial and real market data show that the algorithm is a good alternative in measuring multimodal distribution.}, DOI = {10.31209/2019.100000068} }