Robust volatility updates for Hierarchical Gaussian Filtering
Hierarchical Gaussian Filtering (HGF) networks allow for efficient updating of posterior distributions (beliefs) about hidden states of an agent's environment. HGF parent nodes can target the mean or variance of their children. New information entering at input nodes leads to a cascade of belief updates across the network according to one-step update equations for each node's mean and precision (inverse variance). However, the original form of the update equations for variance-targeting parents(volatility coupling) can in some regions of parameter space lead to negative posterior precision, a logical impossibility which causes the updating algorithm to terminate with an error. In this rep...
| Authors | Christoph Mathys, Nicolas Legrand, Peter Thestrup Waade et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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