Project

Back to overview

Credal Model Averaging for dealing with uncertainty in environmental models

English title Credal Model Averaging for dealing with uncertainty in environmental models
Applicant Rizzoli Andrea Emilio
Number 118071
Funding scheme Project funding
Research institution Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA) IDSIA USI-SUPSI
Institution of higher education University of Applied Sciences and Arts of Southern Switzerland - SUPSI
Main discipline Mathematics
Start/End 01.10.2008 - 30.09.2010
Approved amount 93'975.00
Show all

All Disciplines (2)

Discipline
Mathematics
Other disciplines of Environmental Sciences

Keywords (5)

environmental model uncertainty; environmental model sensitivity; artificial intelligence; imprecise probabilities; model averaging

Lay Summary (English)

Lead
Lay summary
In this project we address the problem of dealing with uncertainty in models of environmental systems. Such systems are often described by complex sets of equations, which tend to be overparametrised. Different models can therefore be calibrated to describe the same phenomenon and the model parameters display a complex interaction structure. A well-founded statistical framework to deal with model uncertainty is Bayesian model averaging (BMA), which prescribes to statistically aggregate the predictions issued by a set of several models, rather than relying on a single model. Yet, an open problem of BMA is how to set the prior distribution, which represents our beliefs about the models. A methodological solution to this problem would require to model prior ignorance, i.e., to regard as feasible all the priors that satisfy some general conditions, rather than ask the investigator to specify a unique, precise prior distribution. To this purpose, we propose to develop CMA (credal model averaging), i.e., to extend BMA to imprecise probabilities (or credal sets). CMA will output a set of posterior distributions (derived from the set or priors) for the variable of interest, rather than a single posterior distribution (derived from a single prior) as in BMA. The developed methodology will be validated by two applications, where model uncertainty plays a key role and nevertheless only little work has been done using BMA. The first one regards epidemiological studies of time series of air pollution and human health; US and European studies report indeed a difference of as much as 50-100% in the estimated air pollution effects, depending on the modelling approach adopted, and point out that there is no valid rule to choose from among them. The second application regards demographic models of animal populations; they are typically characterized by small data sets (for instance, 20 data). Usually, a single model is chosen to issue the predictions; moreover, its structure is also deeply analyzed, in order to point out how the different factors affect the population dynamics. In this case, not accounting for model uncertainty might lead to sub-optimal predictions, and also to unsafe conclusions about the population regulation.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Associated projects

Number Title Start Funding scheme
134759 Credal networks made easy 01.04.2011 Project funding
137680 Learning under near-ignorance: models and methods 01.01.2012 Project funding
146606 Robust structure learning of Bayesian networks 01.01.2014 Project funding
132252 Multi Model Inference for dealing with uncertainty in environmental models 01.10.2010 Project funding

Abstract

In this proposal we want to address the problem of dealing with uncertainty in models of environmental systems. Such systems are often modelled by complex sets of equations, which tend to be overparametrised. Different models can therefore be calibrated to describe the same phenomenon and the model parameters display a complex interaction structure.A well-founded statistical framework to deal with model uncertainty is Bayesian model averaging (BMA), which prescribes to statistically aggregate the predictions issued by a set of several models, rather than relying on a single model. Yet, an open problem of BMA is how to set the prior distribution, which represents our beliefs about the models. A methodological solution to this problem would require to model prior ignorance, i.e., to regard as feasible all the priors that satisfy some general conditions, rather than ask the investigator to specify a unique, precise prior distribution. To this purpose, we propose to develop CMA (credal model averaging), i.e., to extend BMA to imprecise probabilities (or credal sets). CMA will output a set of posterior distributions (derived from the set or priors) for the variable of interest, rather than a single posterior distribution (derived from a single prior) as in BMA.The developed methodology will be validated by two applications, where model uncertainty plays a key role and nevertheless only little work has been done using BMA. The first one regards epidemiological studies of time series of air pollution and human health; US and European studies report indeed a difference of as much as 50-100% in the estimated air pollution effects, depending on the modelling approach adopted, and point out that there is no valid rule to choose from among them.The second application regards demographic models of animal populations; they are typically characterized by small data sets (for instance, 20 data). Usually, a single model is chosen to issue the predictions; moreover, its structure is also deeply analyzed, in order to point out how the different factors affect the population dynamics. In this case, not accounting for model uncertainty might lead to sub-optimal predictions, and also to unsafe conclusions about the population regulation.
-