Publication type: Conference other
Type of review: Not specified
Title: A Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of stochastic differential equation models
Authors: Ulzega, Simone
Conference details: NDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017
Issue Date: Jun-2017
Language: English
Subject (DDC): 003: Systems
Abstract: 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.
Fulltext version: Published version
License (according to publishing contract): Not specified
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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