Mitigating Model Misspecification with Variational Bayesian Inference
Learning dynamical systems from data is an important modelling problem in which one approximates the underlying equations of motion governing the evolution of some system. The conventional approach involves utilising a dynamical model, often derived from expert knowledge to accurately replicate the...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2024
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| Online Access: | https://eprints.nottingham.ac.uk/79405/ |
| _version_ | 1848801115087306752 |
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| author | Krissaane, Ines |
| author_facet | Krissaane, Ines |
| author_sort | Krissaane, Ines |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Learning dynamical systems from data is an important modelling problem in which one approximates the underlying equations of motion governing the evolution of some system. The conventional approach involves utilising a dynamical model, often derived from expert knowledge to accurately replicate the real-world data. This system often involves the inference of interpretable parameters so that model predictions align with observed data. When a chosen model fails to adequately represent the entire unknown dynamic, the ability to extract meaningful information from a fitted model can be challenging. This thesis investigates strategies addressing dynamical model misspecification within the framework of Bayesian inference. We delve into the limitations of standard Bayesian inference methods, specifically for parameter estimation, uncertainty quantification, and prediction accuracy. In our pursuit of a robust inferential approach, we assess the effectiveness of various contemporary methods such as generalised variational methods for dynamic modelling. Additionally, we introduce novel strategies to address model discrepancy, employing both Gaussian Processes and Approximate Bayesian Computation methods. This research aims to advance our understanding of Bayesian inference under model misspecification and offers practical guidance on constructing robust inferential approaches for more accurate and reliable results in continuous-time dynamic process. |
| first_indexed | 2025-11-14T21:02:19Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-79405 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:02:19Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-794052024-12-13T04:40:15Z https://eprints.nottingham.ac.uk/79405/ Mitigating Model Misspecification with Variational Bayesian Inference Krissaane, Ines Learning dynamical systems from data is an important modelling problem in which one approximates the underlying equations of motion governing the evolution of some system. The conventional approach involves utilising a dynamical model, often derived from expert knowledge to accurately replicate the real-world data. This system often involves the inference of interpretable parameters so that model predictions align with observed data. When a chosen model fails to adequately represent the entire unknown dynamic, the ability to extract meaningful information from a fitted model can be challenging. This thesis investigates strategies addressing dynamical model misspecification within the framework of Bayesian inference. We delve into the limitations of standard Bayesian inference methods, specifically for parameter estimation, uncertainty quantification, and prediction accuracy. In our pursuit of a robust inferential approach, we assess the effectiveness of various contemporary methods such as generalised variational methods for dynamic modelling. Additionally, we introduce novel strategies to address model discrepancy, employing both Gaussian Processes and Approximate Bayesian Computation methods. This research aims to advance our understanding of Bayesian inference under model misspecification and offers practical guidance on constructing robust inferential approaches for more accurate and reliable results in continuous-time dynamic process. 2024-12-13 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/79405/1/Mitigating_Model_Misspecification_with_Variational_Bayesian_Inference.pdf Krissaane, Ines (2024) Mitigating Model Misspecification with Variational Bayesian Inference. PhD thesis, University of Nottingham. Model Misspecification Generalised Variational Inference dynamic modelling Bayesian statisics |
| spellingShingle | Model Misspecification Generalised Variational Inference dynamic modelling Bayesian statisics Krissaane, Ines Mitigating Model Misspecification with Variational Bayesian Inference |
| title | Mitigating Model Misspecification with Variational Bayesian Inference |
| title_full | Mitigating Model Misspecification with Variational Bayesian Inference |
| title_fullStr | Mitigating Model Misspecification with Variational Bayesian Inference |
| title_full_unstemmed | Mitigating Model Misspecification with Variational Bayesian Inference |
| title_short | Mitigating Model Misspecification with Variational Bayesian Inference |
| title_sort | mitigating model misspecification with variational bayesian inference |
| topic | Model Misspecification Generalised Variational Inference dynamic modelling Bayesian statisics |
| url | https://eprints.nottingham.ac.uk/79405/ |