Standard Gaussian process regression (GPR) assumes constant noise power throughout the input space and stationarity when combined with the squared exponential covariance function. This can be ...

Explore non-linear regression's role in data science and how it expands beyond linear models' capabilities for complex data analysis.

Nonlinear regression is a form of regression analysis in which data fit to a model is expressed as a mathematical function.

Figure 2 (top panel) shows the TTT plot for the considered data, which is concave indicating an increasing failure rate function, which can be properly accommodated by a Poisson-exponential regression ...

Time series regression Regression models are among the most common types of time series analysis and forecasting techniques.

To address this issue, Yuzhe Yang, Kaiwen Zha, Ying-Cong Chen, Dina Katabi from the Massachusetts Institute of Technology, and Hao Wang from the Rutgers University have introduced Deep Imbalanced ...

Using advanced techniques to make better predictions can help you stand out. Here's a step-by-step guide to learning how to do a regression analysis.