Tag
This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control in nonlinear dynamical systems, using a spiking neural network model demonstrated on a benchmark problem.
This paper introduces a method that uses LLMs combined with prediction markets to measure how information ecosystems bias strategic beliefs, applying it to Ukraine-related markets and finding that English news sources systematically distort territorial predictions.
This paper presents a method to reduce the size of chemical reaction networks (CRNs) implementing probabilistic inference by leveraging factor graph reduction techniques, resulting in smaller CRNs while preserving belief propagation fixed points on surviving variables.