Cluster Prediction for Opinion Dynamics from Partial Observations

Understand

We present a Bayesian approach to predict the clustering of opinions for a system of interacting agents from partial observations.

• The Bayesian formulation overcomes the unobservability of the system and quantifies the uncertainty in the prediction.
• We characterize the clustering by the posterior of the clusters' sizes and centers, and we represent the posterior by samples.
• To overcome the challenge in sampling the high-dimensional posterior, we introduce an auxiliary implicit sampling (AIS) algorithm using two-step observations.