发表机构
Texas A&M University（德州农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ViBR-WM是一种视觉贝叶斯回归世界模型，通过可解释回归和贝叶斯变量选择，在四项预测任务中均优于现有方法，实现更低的物理目标误差。
AI 中文摘要
建模时间依赖性和不确定性是使用世界模型进行预测的核心。视觉贝叶斯回归世界模型通过可解释的回归结合视觉特征、物理历史和已知协变量，并在支持趋势、季节和周期动态的模块化架构内实现。视觉压缩降低了表示维度，而贝叶斯变量选择降低了活跃回归维度。后验预测使用后验概率作为权重，结合了预测器子集的预测，并考虑了参数不确定性和未来扰动。该模型递归地预测联合视觉-物理状态，并直接预测物理目标。在跨越物体运动、植被绿度和太阳能发电的四项预测任务中，ViBR-WM在每项任务上都实现了比Temporal Straightening、ConvLSTM、PredRNN和SimVP更低的平均整体物理目标误差。重复拟合和重采样支持这些整体增益。
英文摘要
Modeling temporal dependence and uncertainty is central to forecasting with world models. The Visual Bayesian Regression World Model combines visual features, physical histories and known covariates through interpretable regression, within a modular architecture supporting trend, seasonal and cycle dynamics. Visual compression reduces representation dimension, while Bayesian variable selection reduces active regression dimension. Posterior prediction combines forecasts across predictor subsets using their posterior probabilities as weights and accounts for parameter uncertainty and future disturbances. The model forecasts joint visual--physical states recursively and physical targets directly. Across four forecasting tasks spanning object motion, vegetation greenness and solar power, ViBR-WM achieves lower mean overall physical-target error than Temporal Straightening, ConvLSTM, PredRNN and SimVP on every task. Repeated fitting and resampling support these overall gains.
