The article discusses the increasing reliance on stochastic gradient (SG) methods for managing large spatial datasets in machine learning and Bayesian inference. These methods allow for efficient gradient estimation using subsamples rather than full datasets, making them scalable. The piece reviews significant advancements in SG Markov Chain Monte Carlo (SG-MCMC) methods that improve posterior sampling from large, independent, and identically distributed data. It highlights the importance of proper gradient scaling, convergence considerations, and practical applications including the analysis of global ocean temperature data.
Stochastic gradient (SG) methods have become essential in handling large datasets, providing efficient gradient estimates using subsamples, thus improving scalability for Bayesian inference.
The SG-MCMC methods enhance posterior sampling by utilizing stochastic techniques, conferring faster convergence and enabling sampling from large-scale datasets effectively.
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