Sarcopenia has a high incidence among patients undergoing maintenance hemodialysis (MHD), significantly increasing the risk of falls, fractures, and mortality. Traditional diagnostic methods, however, ...

The Multi-Output Gaussian Process Toolkit is a Python toolkit for multichannel time series analysis. MOGPTK implements multioutput Gaussian process models with different covariance architectures, ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Compared to standard linear ...

The estimation of Remaining Useful Life (RUL) plays a pivotal role in intelligent manufacturing systems and Industry 4.0 technologies. While recent advancements have improved RUL prediction, many ...

Latent Gaussian models (LGMs) are a staple in the statistical modeling toolkit, especially valuable when dealing with data that exhibits complex, hidden patterns. For engineers, think of LGMs as the ...

Many complex data analysis problems within and beyond the scientific domain involve discovering graphical structures and functional relationships within data. Nonlinear variance decomposition with ...

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...

The aim of this project was to learn the mathematical concepts of Gaussian Processes and implement them later on in real-world problems - in adjusted closing price trend prediction consisted of three ...