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Publication type: Conference poster
Type of review: Not specified
Title: Bayesian parameter inference with stochastic solar dynamo models
Authors: Ulzega, Simone
Albert, Carlo
DOI: 10.21256/zhaw-3225
Conference details: Platform for Advanced Scientific Computing (PASC19), Zurich, 12-14 June 2019
Issue Date: 2019
Publisher / Ed. Institution: ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Language: English
Subjects: Bayesian inference; Stochastic modelling; High-performance computing
Subject (DDC): 003: Systems
510: Mathematics
Abstract: Time-series of cosmogenic radionuclides stored in natural archives such as ice cores and tree rings are a proxy for solar magnetic activity on multi-millennial time-scales. Radionuclides data exhibit a number of interesting features such as intermittent stable cycles of high periods and Grand Minima. Although a lot of effort has gone into the development of sound physically based stochastic solar dynamo models, it is still largely unclear what are the underlying mechanisms that lead to the observed phenomena. Answering these questions requires quantitatively calibrating the models to the data and comparing performances of different models with the associated uncertainties in model parameters and predictions. 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 complex stochastic models feasible. We intend to investigate the power of Approximate Bayesian Computation (ABC) and Hamiltonian Monte Carlo (HMC) algorithms. We present our first inference results with stochastic 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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