Neural-Bayesian Fusion for Real-Time Business Decisioning
Keywords:
Real-time decision support; dynamic environments; hybrid models; neural networks; Bayesian methods; uncertainty quantification; risk assessment; online learning; explainable AI Understanding .Abstract
Growing algorithmic complexity and stringent response requirements inhibit the wide-scale deployment of neural networks for business decision support. While Bayesian methods for quantifying modeling uncertainty introduce latency overheads typically incompatible with real-time operation, hybrid architectures promise the best of both worlds. Yet, existing ensembles lack the necessary expediency. Combined with the need for accurate risk assessment in dynamic environments, a neural-Bayesian model is developed and evaluated for critical support, specifically for decision-making in dynamic environments.
Real-time deployment requires fast inference with low-latency data updates. Meeting these criteria within a hybrid architecture requires special consideration of component data streams and training protocols. In domain-relevant environments, temporal data sparsity enables a direct mapping from the input feature set to the neural subsystem and the early exit of superfluous data from the neural model. The real-time objective also allows for a non-standard online learning procedure involving offline model pretraining, updating during active drift only, and online drift detection with rollback for sudden change. For both systems, the decision-theoretic formulation of risk mitigates the challenge of accurate calibration without the requirement for a dedicated calibration set.
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