التفاصيل البيبلوغرافية
| العنوان: |
Adaptive Bayesian Algorithms for Complex State Space and Mathematical Models |
| المؤلفون: |
Botha, Imke |
| بيانات النشر: |
Queensland University of Technology |
| سنة النشر: |
2024 |
| المجموعة: |
Queensland University of Technology: QUT ePrints |
| مصطلحات موضوعية: |
Bayesian inference, sequential Monte Carlo, particle methods, pseudo-marginal methods, SMC^2, ensemble Kalman inversion, state space models |
| الوصف: |
Calibrating statistical models to data can be a challenging task, particularly when the model is difficult or time consuming to evaluate. Methods that infer the parameters of these models often require significant practitioner effort and/or computational resources to run, or make simplifying assumptions about the model. This thesis develops novel parameter inference methods that address each of these issues. The new methods require minimal practitioner input, are faster to run, and can be applied in more general settings. |
| نوع الوثيقة: |
thesis |
| وصف الملف: |
application/pdf |
| اللغة: |
unknown |
| Relation: |
https://eprints.qut.edu.au/251487/1/Imke%20Botha%20Thesis.pdf; Botha, Imke (2024) Adaptive Bayesian Algorithms for Complex State Space and Mathematical Models. PhD by Publication, Queensland University of Technology.; https://eprints.qut.edu.au/251487/; Faculty of Science |
| الاتاحة: |
https://eprints.qut.edu.au/251487/ |
| Rights: |
free_to_read ; http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| رقم الانضمام: |
edsbas.79B23CA8 |
| قاعدة البيانات: |
BASE |