A common problem in statistical inference is the need to unify distributed analyses and inferences on shared parameters from multiple sources into a single coherent inference. This problem can appear in settings such as expert elicitation, distributed ‘big data’ problems, and tempering. In this talk, I will introduce some popular algorithms for this task. In particular, I will focus on Monte Carlo Fusion [Dai, Pollock & Roberts, 2018] which proposes a new theory and methodology to tackle this problem.