SEMIPARAMETRIC MEAN FIELD VARIATIONAL BAYES Professor Matt Wand University of Technology, Sydney A pervasive theme impacting Statistics in the mid-2010s is the increasing prevalence of data that are big in terms of volume and/or velocity. Mean field variational Bayes methodology is one means of confronting this sea-change. It delivers fast approximate Bayesian inference in real time. Vanilla mean field variational Bayes is inherently *nonparametric* in that the functional forms of the approximate posterior density functions that it produces depend only on the mean field restrictions. However, these forms can be quite complicated when non-conjugacies arise. A possible remedy is *semiparametric* mean field variational Bayes, in which the complicated functional forms are replaced by simpler forms at the outset. This leads to new challenges. Some of these will be discussed, solved and illustrated for certain non-standard regression models.