A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

29 May 2018Changyou ChenRuiyi ZhangWenlin WangBai LiLiqun Chen

There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis. A standard SG-MCMC algorithm simulates samples from a discrete-time Markov chain to approximate a target distribution, thus samples could be highly correlated, an undesired property for SG-MCMC... (read more)

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