Challenges and questions

摘要

In this review, we have examined a variety of different views on scaling Bayesian inference up to large datasets and greater model complexity and out to parallel compute resources. Several different themes have emerged, from techniques that exploit subsets of data for computational savings to proposals for distributing inference computations across multiple machines. Progress is being made, but there remain significant open questions and outstanding challenges to be tackled as this research programme moves forward.