Dynamic linear models: a Bayesian computational approach
Palade, Viviana Luisa (A.A. 2024/2025) Dynamic linear models: a Bayesian computational approach. Tesi di Laurea in Machine learning, Luiss Guido Carli, relatore Marta Catalano, pp. 201. [Master's Degree Thesis]
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Abstract/Index
Dynamic linear models. State-space models and dynamic linear models. Modular components of dynamic linear models. Statistical inference. Formal derivations and additional remarks. Computational techniques. Frequentist estimation. Bayesian estimation. Frequentist vs Bayesian inference: a unified view. Empirical analysis with dynamic linear models. Data description. Model specification. Forecast evaluation design and metrics. Uncertainty quantification. Local linear trend (LLT). ARIMA (0,1,1): reduced–form benchmark. Bayesian approach.
References
Bibliografia: pp. 189-193.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Economics and Finance (LM-56) |
| Chair: | Machine learning |
| Thesis Supervisor: | Catalano, Marta |
| Thesis Co-Supervisor: | Patnaik, Megha |
| Academic Year: | 2024/2025 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 20 Feb 2026 14:41 |
| Last Modified: | 20 Feb 2026 14:41 |
| URI: | https://tesi.luiss.it/id/eprint/44920 |
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