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The True Cost of Stochastic Gradient Langevin Dynamics
[article]
2017
arXiv
pre-print
The problem of posterior inference is central to Bayesian statistics and a wealth of Markov Chain Monte Carlo (MCMC) methods have been proposed to obtain asymptotically correct samples from the posterior. As datasets in applications grow larger and larger, scalability has emerged as a central problem for MCMC methods. Stochastic Gradient Langevin Dynamics (SGLD) and related stochastic gradient Markov Chain Monte Carlo methods offer scalability by using stochastic gradients in each step of the
arXiv:1706.02692v1
fatcat:p5uar7tafzh5rc5eiziublwjw4