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Parnell, Andrew, Sweeney, James, Doan, Thinh K., Salter-Townshend, Michael, Allen, Judy R. M., Huntley, Brian and Haslett, John (2014) Bayesian Inference for Palaeoclimate with time Uncertainty and Stochastic Volatility. Journal of the Royal Statistical Society Series C: Applied Statistics, 64 (1). pp. 115-138. ISSN 0035-9254
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Abstract
We propose and fit a Bayesian model to infer palaeoclimate over several thousand
years. The data that we use arise as ancient pollen counts taken from sediment cores together
with radiocarbon dates which provide (uncertain) ages. When combined with a modern pollen–
climate data set, we can calibrate ancient pollen into ancient climate. We use a normal–inverse
Gaussian process prior to model the stochastic volatility of palaeoclimate over time, and we
present a novel modularized Markov chain Monte Chain algorithm to enable fast computation.
We illustrate our approach with a case-study from Sluggan Moss, Northern Ireland, and provide
an R package, Bclim, for use at other sites.
| Item Type: | Article |
|---|---|
| Keywords: | Hierarchical time series; Modular Bayes; Normal–inverse Gaussian process; Palaeoclimate reconstruction; Temporal uncertainty; |
| Academic Unit: | Faculty of Science and Engineering > Mathematics and Statistics Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS |
| Item ID: | 19112 |
| Identification Number: | 10.1111/rssc.12065 |
| Depositing User: | Andrew Parnell |
| Date Deposited: | 29 Oct 2024 12:03 |
| Journal or Publication Title: | Journal of the Royal Statistical Society Series C: Applied Statistics |
| Publisher: | The Royal Statistical Society |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/19112 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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