Robust volatility updates for Hierarchical Gaussian Filtering

TL;DR

This paper introduces a robust modification to Hierarchical Gaussian Filtering that prevents negative posterior precision, ensuring stable belief updates across all parameter regions.

Contribution

A new quadratic approximation method for volatility-coupled nodes in HGF that guarantees positive posterior precision and stable updates.

Findings

The modified update equations prevent negative posterior precision.

The approach remains accurate even with large prediction errors.

The method is robust across the entire parameter space.

Abstract

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 report, we introduce a modified quadratic approximation to the variational energy of volatility-coupled nodes that avoids negative posterior precision. The key idea is to interpolate between two quadratic expansions of the variational energy: one at the prior prediction and one at a second mode whose location is obtained in closed form via the Lambert W function. The resulting update equations are robust across the entire parameter space and faithfully track the variational posterior even for large prediction errors.