In statistical modeling, parameter estimation is an essential and challengeable task.
Estimation of the parameters in the Dirichlet mixture model (DMM) is analytically intractable …

Abstract Model overfitting and data imbalance are two main challenges in radiomics studies.
In this study, we develop a high-generalizable classifier MERGE (Multi-kErnel Regression …

This article focuses on the adaptive Kalman filtering problem for linear systems with
unknown covariances of both dynamic multiplicative noise (multiplicative measurement …

The distributed nature of power electronic components parameters can affect the desired
output voltage of the CLLC-type dual active bridge (DAB) converters, especially in mass …

A Bayesian nonparametric approach for estimation of a Dirichlet process (DP) mixture of
generalized inverted Dirichlet distributions [ie, an infinite generalized inverted Dirichlet …

Featured Application This article creates a paradigm for future studies on measuring the
determinants of ICT fluency and the evolution of the digital divide among higher education …

As a variant of Finite mixture model (FMM), finite Inverted Dirichlet mixture model (IDMM)
can not avoid the conventional challenges, such as how to select the appropriate number of …

Social influence prediction has permeated many domains, including marketing, behavior
prediction, recommendation systems, and more. However, traditional methods of predicting …

Color reproduction is important in many tasks such as images display on the web,
photography and textile industry. Ideally, the data about the color space in which an image is …

In dynamic environments, most simultaneous localization and mapping (SLAM) methods
face challenges due to potential erroneous data associations caused by the movement of …