Publication type: Conference other
Type of review: Not specified
Title: Bayesian inference methods for the calibration of stochastic dynamo models
Authors: Ulzega, Simone
Albert, Carlo
et. al: No
Conference details: 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019
Issue Date: 2019
Language: English
Subject (DDC): 003: Systems
510: Mathematics
Abstract: In essentially all applied sciences, data-driven modeling heavily relies on a sound calibration of model parameters to measured data for understanding the underlying mechanisms that lead to observed features. Solar dynamo models are no exception. Bayesian statistics is a consistent framework for parameter inference where knowledge about model parameters is expressed through probability distributions and updated using measured data. However, Bayesian inference with non-linear stochastic models can become computationally extremely expensive and it is therefore hardly ever applied. In recent years, sophisticated and scalable algorithms have emerged, which have the potential of making Bayesian inference for stochastic models feasible. We investigate the power of Approximate Baysian Computation (ABC), enhanced by Machine Learning methods, and Hamiltonian Monte Carlo algorithms applied to solar dynamo models.
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)
Published as part of the ZHAW project: BISTOM - Bayesian Inference with Stochastic Models
Appears in collections:Publikationen Life Sciences und Facility Management

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