Particle-based Online Bayesian Sampling

Online learning has gained increasing interest due Online optimization methods can directly be applied to update to its capability of tracking real-world streaming models that are fully specified by a certain value of its data. Although it has been widely studied in the parameters. Beyond such models, there is another class of setting of frequentist statistics, few works have models known as Bayesian models that treat the parameters considered online learning with the Bayesian sampling as random variables, thus giving an output also as a random problem. In this paper, we study an Online variable (often the expectation is taken as the final output on Particle-based Variational Inference (OPVI) algorithm par with the conventional case). The stochasticity enables that updates a set of particles to gradually Bayesian models to provide diverse outputs, characterize approximate the Bayesian posterior. To reduce prediction uncertainty, and be more robust to adversarial the gradient error caused by the use of stochastic attacks (Hernández-Lobato and Adams, 2015; Li and Gal, approximation, we include a sublinear increasing 2017; Yoon et al., 2018; Zhang et al., 2019; Tolpin et al., batch-size method to reduce the variance.