The coupling of HIGRAD with BEHAVE was used in successful simulations of actual fires in Colorado and California (Reisner et al., 1998). These empirical-dynamical models begin to capture many of the features of real fires, but they are still limited by the empirically based fire behavior scheme. Game drivers download for pc - Best answers Graphic card for laptop free download - Best answers Nvidia graphics driver scanner - How-To - Graphic cards.
Uncertainty Quantification for Online Learning and Stochastic Approximation via Hierarchical Incremental Gradient Descent
Stochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However, despite an ever- increasing volume of work on SGD, much less is known about the statistical inferential properties of SGD-based predictions. Taking a fully inferential viewpoint, this paper introduces a novel procedure termed HiGrad to conduct statistical inference for online learning, without incurring additional computational cost compared with SGD. The HiGrad procedure begins by performing SGD updates for a while and then splits the single thread into several threads, and this procedure hierarchically operates in this fashion along each thread. With predictions provided by multiple threads in place, a t-based confidence interval is constructed by decorrelating predictions using covariance structures given by a Donsker-style extension of the Ruppert--Polyak averaging scheme, which is a technical contribution of independent interest. Under certain regularity conditions, the HiGrad confidence interval is shown to attain asymptotically exact coverage probability. Finally, the performance of HiGrad is evaluated through extensive simulation studies and a real data example. An R package higrad has been developed to implement the method.
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- Statistics - Machine Learning;
- Computer Science - Distributed;
- and Cluster Computing;
- Mathematics - Optimization and Control;
- Statistics - Methodology