Heteroscedastic Regression

Heteroscedastic regression models address the challenge of predicting not only the mean of a variable but also its variance, acknowledging that the uncertainty of predictions can vary depending on the input data. Current research focuses on improving the accuracy and calibration of these models, particularly through the development of novel neural network architectures like Deep Double Poisson Networks and the refinement of existing methods such as quantile regression and Gaussian processes. These advancements are crucial for enhancing the reliability of predictions in diverse fields, from time series forecasting and pricing strategies to image classification and scientific simulations, where accurate uncertainty quantification is paramount.