background: birth weight and gestational age are two important variables in obstetric research. the primary measure of gestational age is based on a mother's recall of her last menstrual period. this recall may cause random or systematic errors. therefore, the objective of this study is to utilize bayesian mixture model in order to identify implausible gestational age.   methods: in this cross-...

An efficient Bayesian hierarchical model for spatial extremes on a large domain is proposed. In the data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is applied. Spatial dependence in the GEV parameters are captured with a latent spatial regression. Using a composite likelihood approach and a method for incorporating stations with missing data, we are able...

The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series. Segmentation is then conducted using a hierarchical Bayesian model that accounts for correlations between the wind speed and direction. A Gibbs sampling strategy overcomes the computational complexity of ...

This paper presents a new Bayesian non-parametric model by extending the usage of Hierarchical Latent Dirichlet Allocation to extract tree structured word clusters from text data. The inference algorithm of the model collects words in a cluster if they share similar distribution over documents. In our experiments, we observed meaningful hierarchical structures on NIPS corpus and radiology repor...

In recent years, the automated driving system has been known to be one of most popular research topics artificial intelligence (AI) and intelligent transportation (ITS). The journey experience on vehicles could improved by individualization understanding. Although previous studies have proposed methods for styles understanding, classification not addressed thoroughly. Therefore, in this study, ...