论文标题
从部分观察到意见动态的群集预测
Cluster Prediction for Opinion Dynamics from Partial Observations
论文作者
论文摘要
我们提出了一种贝叶斯的方法，可以预测部分观察的相互作用制度系统的意见聚类。贝叶斯公式克服了系统的不可观察性，并量化了预测中的不确定性。我们通过簇的大小和中心的后部来表征聚类，我们代表样品后部。为了克服对高维后验的挑战，我们使用两步观测引入了辅助隐式采样（AIS）算法。数值结果表明，在无嘈杂和嘈杂的观察情况下，AIS算法可以准确地预测主要簇的大小和中心。特别是，这些中心的成功率很高，但是尺寸显示出对观察噪声和观察比敏感的相当不确定性。
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. Numerical results show that the AIS algorithm leads to accurate predictions of the sizes and centers for the leading clusters, in both cases of noiseless and noisy observations. In particular, the centers are predicted with high success rates, but the sizes exhibit a considerable uncertainty that is sensitive to observation noise and the observation ratio.
