Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 554-570 |
| Number of pages | 17 |
| Journal | Journal of Agricultural, Biological, and Environmental Statistics |
| Volume | 16 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1 Dec 2011 |
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Bibliographical note
The definitive version is available at onlinelibrary.wiley.com© Journal, International Biometric Society and Wiley-Blackwell
Keywords
- model error
- rainfall-runoff model
- Monte Carlo EM algorithm
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Quantifying simulator discrepancy in discrete-time dynamical simulators. / Wilkinson, Richard D.; Vrettas, Michail; Cornford, Dan; Oakley, Jeremy E.
In: Journal of Agricultural, Biological, and Environmental Statistics, Vol. 16, No. 4, 01.12.2011, p. 554-570.Research output: Contribution to journal › Article
TY - JOUR
T1 - Quantifying simulator discrepancy in discrete-time dynamical simulators
AU - Wilkinson, Richard D.
AU - Vrettas, Michail
AU - Cornford, Dan
AU - Oakley, Jeremy E.
N1 - The definitive version is available at onlinelibrary.wiley.com © Journal, International Biometric Society and Wiley-Blackwell
PY - 2011/12/1
Y1 - 2011/12/1
N2 - When making predictions with complex simulators it can be important to quantify the various sources of uncertainty. Errors in the structural specification of the simulator, for example due to missing processes or incorrect mathematical specification, can be a major source of uncertainty, but are often ignored. We introduce a methodology for inferring the discrepancy between the simulator and the system in discrete-time dynamical simulators. We assume a structural form for the discrepancy function, and show how to infer the maximum-likelihood parameter estimates using a particle filter embedded within a Monte Carlo expectation maximization (MCEM) algorithm. We illustrate the method on a conceptual rainfall-runoff simulator (logSPM) used to model the Abercrombie catchment in Australia. We assess the simulator and discrepancy model on the basis of their predictive performance using proper scoring rules. This article has supplementary material online.
AB - When making predictions with complex simulators it can be important to quantify the various sources of uncertainty. Errors in the structural specification of the simulator, for example due to missing processes or incorrect mathematical specification, can be a major source of uncertainty, but are often ignored. We introduce a methodology for inferring the discrepancy between the simulator and the system in discrete-time dynamical simulators. We assume a structural form for the discrepancy function, and show how to infer the maximum-likelihood parameter estimates using a particle filter embedded within a Monte Carlo expectation maximization (MCEM) algorithm. We illustrate the method on a conceptual rainfall-runoff simulator (logSPM) used to model the Abercrombie catchment in Australia. We assess the simulator and discrepancy model on the basis of their predictive performance using proper scoring rules. This article has supplementary material online.
KW - model error
KW - rainfall-runoff model
KW - Monte Carlo EM algorithm
UR - http://www.scopus.com/inward/record.url?scp=83555177262&partnerID=8YFLogxK
U2 - 10.1007/s13253-011-0077-3
DO - 10.1007/s13253-011-0077-3
M3 - Article
AN - SCOPUS:83555177262
VL - 16
SP - 554
EP - 570
JO - Journal of Agricultural, Biological, and Environmental Statistics
JF - Journal of Agricultural, Biological, and Environmental Statistics
SN - 1085-7117
IS - 4
ER -