ViBR-WM: Visual Bayesian Regression for World Modeling
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 dire...
| Authors | Jifan Li, Ning Ning |
|---|---|
| Primary category | stat.ME |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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