Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

2019 ◽  
Vol 120 (4) ◽  
pp. 447-472 ◽  
Author(s):  
Paul‐Baptiste Rubio ◽  
François Louf ◽  
Ludovic Chamoin
Keyword(s):  
Data Assimilation ◽  
Model Reduction ◽  
Bayesian Data Assimilation

scholarly journals Bayesian data assimilation provides rapid decision support for vector-borne diseases

2015 ◽  
Vol 12 (108) ◽  
pp. 20150367 ◽  
Author(s):  
Chris P. Jewell ◽  
Richard G. Brown
Keyword(s):  
Decision Support ◽  
Data Assimilation ◽  
Population Data ◽  
Disease Status ◽  
Host Population ◽  
Significant Advance ◽  
Model Parameters ◽  
Vector Borne Diseases ◽  
Vector Borne ◽  
Bayesian Data Assimilation

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks.

The effects of model reduction and data assimilation on greenhouse climate predictions

2017 ◽  
pp. 235-242
Author(s):  
S. van Mourik ◽  
P.J.M. van Beveren ◽  
I.L. López-Cruz ◽  
E.J. van Henten
Keyword(s):  
Data Assimilation ◽  
Model Reduction ◽  
Greenhouse Climate

scholarly journals Bayesian data assimilation in shape registration

2013 ◽  
Vol 29 (4) ◽  
pp. 045011 ◽  
Author(s):  
C J Cotter ◽  
S L Cotter ◽  
F-X Vialard
Keyword(s):  
Data Assimilation ◽  
Shape Registration ◽  
Bayesian Data Assimilation

scholarly journals Estimation of land-use change using a Bayesian data assimilation approach

2017 ◽  
Author(s):  
Peter Levy ◽  
Marcel Van Oijen ◽  
Gwen Buys ◽  
Sam Tomlinson
Keyword(s):  
Land Use ◽  
Data Assimilation ◽  
Land Use Change ◽  
Census Data ◽  
Likelihood Function ◽  
Skewed Distribution ◽  
Data Sources ◽  
Modelling Uncertainty ◽  
Bayesian Data Assimilation ◽  
Posterior Estimate

Abstract. We present a method for estimating land-use change using a Bayesian data assimilation approach. The approach provides a general framework for combining multiple disparate data sources with a simple model. This allows us to constrain estimates of gross land-use change with reliable national-scale census data, whilst retaining the detailed information available from several other sources. Eight different data sources, with three different data structures, were combined in our posterior estimate of land-use and land-use change, and other data sources could easily be added in future. The tendency for observations to underestimate gross land-use change is accounted for by allowing for a skewed distribution in the likelihood function. The data structure produced has high temporal and spatial resolution, and is appropriate for dynamic process-based modelling. Uncertainty is propagated appropriately into the output, so we have a full posterior distribution of output and parameters. The data are available in the widely used netCDF file format from http://eidc.ceh.ac.uk/ (doi pending).

scholarly journals Overview: Data Assimilation and Model Reduction

PAMM ◽  
2007 ◽  
Vol 7 (1) ◽  
pp. 1026501-1026502
Author(s):  
N.K. Nichols
Keyword(s):  
Data Assimilation ◽  
Model Reduction
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