@inproceedings{1591d6c071c2464588fed60ebaf1a2ad,
title = "Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children",
abstract = "The time developments of BMIs (Body Math Index) of children are known to be classified into several types, and the results can be utilized to their health guidance. For this purpose, we approach to the classification problem of the time developments of the BMI data of Japanese children from their babyhood to junior high school age. We have inferred the dimension of their principal component space and the number of component distributions of a Gaussian mixture model, adopting a framework of variational Bayesian statistical inference. As a result, the data are found to be classified into 8 types in a 12-dimensional principal component space.",
keywords = "Bayes, BMI, Classification, GMM, PCA, time series, variational approximation",
author = "Toshiaki Aida and Chiyori Haga",
year = "2019",
month = may,
day = "10",
doi = "10.1109/ICBDA.2019.8713200",
language = "English",
series = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "21--25",
booktitle = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",
note = "4th IEEE International Conference on Big Data Analytics, ICBDA 2019 ; Conference date: 15-03-2019 Through 18-03-2019",
}