Book chapter · 2026
Bayesian Approaches for Imputing Missing Data in Environmental Studies: Hierarchical Models and Kalman Filter Techniques
Zeki Bora Ön, Sena Akçer-Ön · In An Ocean of Gradients – Towards a 3D Mapping and Visualization of High-Resolution Marine Data, pp. 135–144. CIESM. Edited by Laura Giuliano, A. Rodriguez y Baena.
What this chapter covers
The chapter presents three approaches: Bayesian hierarchical regression, hierarchical regression with change points, and Bayesian Kalman filtering combined with variable selection. Examples illustrate how these methods can reconstruct gaps in environmental series.
When this chapter may be useful
- Missing environmental observations
- Bayesian hierarchical imputation
- Change-point-aware regression
- Kalman filtering and variable selection
Methods and keywords
Suggested citation
Zeki Bora Ön, Sena Akçer-Ön (2026). Bayesian Approaches for Imputing Missing Data in Environmental Studies: Hierarchical Models and Kalman Filter Techniques. In An Ocean of Gradients – Towards a 3D Mapping and Visualization of High-Resolution Marine Data, pp. 135–144. CIESM.