Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ulzega, Simone | - |
| dc.contributor.author | Albert, Carlo | - |
| dc.date.accessioned | 2019-12-19T10:00:31Z | - |
| dc.date.available | 2019-12-19T10:00:31Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/18948 | - |
| dc.description.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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.rights | Not specified | de_CH |
| dc.subject.ddc | 003: Systeme | de_CH |
| dc.subject.ddc | 510: Mathematik | de_CH |
| dc.title | Bayesian inference methods for the calibration of stochastic dynamo models | de_CH |
| dc.type | Konferenz: Sonstiges | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | Life Sciences und Facility Management | de_CH |
| zhaw.organisationalunit | Institut für Computational Life Sciences (ICLS) | de_CH |
| zhaw.conference.details | 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Not specified | de_CH |
| zhaw.webfeed | Biomedical Simulation | de_CH |
| zhaw.funding.zhaw | BISTOM - Bayesian Inference with Stochastic Models | de_CH |
| zhaw.author.additional | No | de_CH |
| Appears in collections: | Publikationen Life Sciences und Facility Management | |
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Ulzega, S., & Albert, C. (2019). Bayesian inference methods for the calibration of stochastic dynamo models. 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.
Ulzega, S. and Albert, C. (2019) ‘Bayesian inference methods for the calibration of stochastic dynamo models’, in 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.
S. Ulzega and C. Albert, “Bayesian inference methods for the calibration of stochastic dynamo models,” in 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019, 2019.
ULZEGA, Simone und Carlo ALBERT, 2019. Bayesian inference methods for the calibration of stochastic dynamo models. In: 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019. Conference presentation. 2019
Ulzega, Simone, and Carlo Albert. 2019. “Bayesian Inference Methods for the Calibration of Stochastic Dynamo Models.” Conference presentation. In 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.
Ulzega, Simone, and Carlo Albert. “Bayesian Inference Methods for the Calibration of Stochastic Dynamo Models.” 4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019, 2019.
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