Abstract
This paper shows how the maximum (information) entropy principle allows the derivation of the short-time propagator from experimental data provided the process is Markovian. From the propagator, the Fokker-Planck equation can be derived. The Lagrange parameters that are used in the maximum information entropy principle can be derived by minimizing the Kullback information.
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This section generalizes Sect. 9.3 of [3] from the one-dimensional to the multidimensional case
This section generalizes the one-dimensional case treated in [6] to the multidimensional case
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Haken, H. (2003). Application of the Maximum (Information) Entropy Principle to Stochastic Processes far from Thermal Equilibrium. In: Karmeshu (eds) Entropy Measures, Maximum Entropy Principle and Emerging Applications. Studies in Fuzziness and Soft Computing, vol 119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36212-8_3
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DOI: https://doi.org/10.1007/978-3-540-36212-8_3
Publisher Name: Springer, Berlin, Heidelberg
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