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Multi Model Inference for dealing with uncertainty in environmental models

English title Multi Model Inference for dealing with uncertainty in environmental models
Applicant Rizzoli Andrea Emilio
Number 132252
Funding scheme Project funding (Div. I-III)
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 01.10.2010 - 30.09.2012
Approved amount 93'210.00
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All Disciplines (2)

Discipline
Mathematics
Other disciplines of Environmental Sciences

Keywords (5)

model uncertainty; ecological modelling; artificial intelligence; imprecise probabilities; multi model inference

Lay Summary (English)

Lead
Lay summary
Mathematical models are inherently approximate descriptions of the reality. Such an inherent imprecision is particularly relevant for models of the environment and of natural processes. In many environmental models uncertainty is overly epistemic, that is, due to the lack of knowledge on certain processes and mechanisms. As epistemic uncertainty becomes more relevant, alternative model formulations can be proposed for describing the same process.In presence of alternative model formulations the problem of model selection arises. Typical criteria for model selection are based on scores provided by BIC (Bayesian Information Criterion) or AIC (Akaike Information Criterion). Often the "winning" model is selected for future use, thus disregarding alternative yet plausible models.Alternatively, model averaging considers several models at the same time, weighting each model proportionally to the support it receives from the data. Model averaging can be thought as a soft model selection: the investigator weighs the model according to how likely they are, without sharply selecting a single candidate. Using model averaging rather than a single model corresponds to ask the opinion of a committee of experts rather than that of a single expert, which is common wisdom when taking critical decisions.A critique of model averaging lies in the strong dependency of the conclusions on the prior probability of the models, especially when the data are scarce.This projects continues a previous SNF funded project aimed at the development of a credal model averaging framework (CMA) to address the problem of the choice of the prior over the models in BMA, by adopting a set of prior distributions over the models. Technically, CMA will constitute an extension of BMA to imprecise probabilities. The prior credal set will express very weak beliefs about the relative credibility a priori of the different models.As a result of the prior credal set, CMA will estimate the posterior probability of each model as belonging to an interval. Moreover, CMA will return predictions in the form of an interval, unlike traditional regressors which return point-wise predictions. In particular, the inferences produces by CMA will encompass those produced by model averaging using both AIC and BIC.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
Evaluating credal classifiers by utility-discounted predictive accuracy. International Journal of Approximate Reasoning
Zaffalon Marco, Corani Giorgio, Mauá Denis (2012), Evaluating credal classifiers by utility-discounted predictive accuracy. International Journal of Approximate Reasoning, in International Journal of Approximate Reasoning , 53(8), 1282-1301.
A Bayesian Network model for predicting the outcome of in vitro fertilization
Corani Giorgio, Magli C., Giusti Alessandro, Gianaroli L., Gambardella L.M. (2012), A Bayesian Network model for predicting the outcome of in vitro fertilization, in Proc. of the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012), Granada, Spain.
Active Learning by the Naive Credal Classifier
Antonucci A., Corani G., Gabaglio S. (2012), Active Learning by the Naive Credal Classifier, in Proc. of the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012), Granada, Spain.
Compression-based AODE classifiers
Corani G., Antonucci A., De Rosa R. (2012), Compression-based AODE classifiers, in Proc. 20th European Conference on Artificial Intelligence (ECAI 2012), Montpellier, France.
Likelihood-Based Robust Classification with Bayesian Networks
Antonucci A., Cattaneo M.E.G.V., Corani G. (2012), Likelihood-Based Robust Classification with Bayesian Networks, in Greco S., Bouchon-Meunier B., Coletti G., Fedrizzi M., Matarazzo B., Yager R.R. (ed.), Springer, Berlin, 491-500.
Anytime marginal map inference
Mauà D.D., de Campos C.P. (2012), Anytime marginal map inference, in Proc. of the International Conference on Machine Learning (ICML 2012), Edinburgh, Scotland.
Credal model averaging: dealing robustly with model uncertainty on small data sets
Mignatti A., Corani G., Rizzoli A.E. (2012), Credal model averaging: dealing robustly with model uncertainty on small data sets, in Proc. 6th International Congress on Environmental Modelling and Software (iEMSs 2012), Leipzig.
Solving limited memory influence diagrams
Mauá D.D., de Campos C.P., Zaffalon M. (2012), Solving limited memory influence diagrams, in Journal of Artificial Intelligence Research, 44, 97-140.
Updating credal networks is approximable in polynomial time
Mauá D.D., de Campos C.P., Zaffalon M. (2012), Updating credal networks is approximable in polynomial time, in International Journal of Approximate Reasoning, 53(8), 1183-1199.
Likelihood-Based Naive Credal Classifier
Antonucci Alessandro, Cattaneo Marco E. G. V., Corani Giorgio (2011), Likelihood-Based Naive Credal Classifier, in Proceedings ISIPTA 2011, Innsbruck, AustriaSIPTA, Innsbrück, Austria.
Utility-Based Accuracy Measures to Empirically Evaluate Credal Classifiers
Zaffalon Marco, Corani Giorgio, Mauá Denis (2011), Utility-Based Accuracy Measures to Empirically Evaluate Credal Classifiers, in ISIPTA 2011.
Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification.
Corani Giorgio, Antonucci Alessandro, Zaffalon Marco (2011), Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification., in Holmes E. D., Lakhmi J.C. (ed.), Springer, Berlin, 49-93.

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
PGM2012 19.09.2012 Granada, España
ECAI 2012 27.08.2012 Montpellier, France
IPMU2012 05.07.2012 Catania, Italy
6th International Congress on Environmental Modelling and Software (iEMSs) 05.07.2012 Leipzig, Deutschland
ICML 2012 26.06.2012 Edinburgh, cotland
ISIPTA’11 7th international symposium on imprecise probability: theories and applications 25.07.2011 Innsbrück, Austria


Associated projects

Number Title Start Funding scheme
134759 Credal networks made easy 01.04.2011 Project funding (Div. I-III)
146606 Robust structure learning of Bayesian networks 01.01.2014 Project funding (Div. I-III)
137680 Learning under near-ignorance: models and methods 01.01.2012 Project funding (Div. I-III)
118071 Credal Model Averaging for dealing with uncertainty in environmental models 01.10.2008 Project funding (Div. I-III)

Abstract

This proposal builds on a previous SNF project titled "Credal Model Averaging for dealing with uncertainty in environmental models". On the basis of the results obtained in that project, we want to address the problem posed when several competing models reasonably intepret the data and nevertheless lead to different conclusions about the quantities of interest. This is a common problem in environmental modelling, and in those disciplines where the role of uncertainty is most relevant.
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