Large Dataset Analysis

In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a scoring criterion that favors the simplest structure for which the model is able to represent the ...

Factor-based forecasting has been at the forefront of developments in the macroeconometric forecasting literature in the recent past. Despite the flurry of activity in the area, a number of specification issues such as the choice of the number of factors in the forecasting regression, the benefits of combining factor‐based forecasts and the choice of the dataset from which to extract

Abstract—Two-dimensional (2-D) phase unwrapping is a key step in the analysis of interferometric synthetic aperture radar (InSAR) data. While challenging even in the best of circumstances, this problem poses unique difficulties when the dimensions of the interferometric input data ...

People display regularities in almost everything they do. This paper proposes characteristics of an idealized algorithm that, when applied to sequences of user actions, would allow a user interface to adapt over time to an individual's pattern of use. We describe a simple predictive method with these characteristics and show its predictive accuracy on a large dataset of UNIX commands to be at least as good as others that have been considered, while using fewer computational and memory resources.