Probabilistic Inference for Dynamical Systems
Sergio Davis1,2, Diego González1,3, Gonzalo Gutiérrez3
1Comisión Chilena de Energía Nuclear, Casilla 188-D Santiago, Chile.
Abstract:
A general framework for inference in dynamical systems is described, based on the language of Bayesian probability theory and making use of the maximum entropy principle. Taking the concept of a path as fundamental, the continuity equation and Cauchy's equation for fluid dynamics arise naturally, while the specific information about the system can be included using the maximum caliber (or maximum path entropy) principle.
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