Publikationstyp: Konferenz: Sonstiges
Art der Begutachtung: Keine Angabe
Titel: A Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of stochastic differential equation models
Autor/-in: Ulzega, Simone
Angaben zur Konferenz: NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017
Erscheinungsdatum: Jun-2017
Sprache: Englisch
Fachgebiet (DDC): 003: Systeme
Zusammenfassung: Parameter inference is a fundamental problem in data-driven modeling. Indeed, for making reliable probabilistic predictions, model parameters need to be calibrated to measured data and their uncertainty needs to be quantified. Bayesian statistics is a consistent framework where knowledge about parameters is expressed through probability distributions. These so-called posterior distributions can become computationally very expensive to evaluate, especially with non-trivial stochastic models. We present a novel Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of nonlinear stochastic differential equation models. The algorithm relies on the reinterpretation of the posterior distribution as the partition function of a statistical mechanics system akin to a polymer. We thus reduce the Bayesian inference problem to simulating the dynamics of a fictitious linear molecule whose dynamics are confined by the data and the model. Our approach is very efficient, applicable to a wide range of inference problems and highly parallelizable.
Volltext Version: Publizierte Version
Lizenz (gemäss Verlagsvertrag): Keine Angabe
Departement: Life Sciences und Facility Management
Organisationseinheit: Institut für Computational Life Sciences (ICLS)
Enthalten in den Sammlungen:Publikationen Life Sciences und Facility Management

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