Neural Bayesian Sequential Routing (NBSR) is a new framework modeling neural inference as active evidence accumulation over a hierarchical Directed Acyclic Graph. It utilizes a Dirichlet–Categorical conjugate framework to update a Dirichlet belief state, enabling path-dependent routing and uncertainty quantification.
The Distill-Belief framework addresses the challenge of efficient and accurate inverse source localization and characterization (ISLC) for mobile agents by balancing correctness and efficiency. It proposes a teacher-student model, where a Bayes-correct particle filter teacher guides a compact student for fast, uncertainty-aware decision-making in real-time.
Over the centuries, probability theory has grown from the calculus of games of chance into a central framework for reasoning under uncertainty. This article interprets that evolution as a transformation of rationality itself, culminating in modern Bayesian inference.