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COMIP-IJAI 2017 : Special Issue of IJAI - Combinatorial Optimization Methods for Inverse Problems


When N/A
Where N/A
Abstract Registration Due Dec 30, 2016
Submission Deadline Feb 28, 2017
Notification Due Apr 30, 2017
Final Version Due May 30, 2017
Categories    computer science   inverse problems   combinatorial optimization   data assimilation

Call For Papers

Inverse Problems are problems related to parameter and state estimation (inputs) based on (possibly) real-noisy observations (perturbed outputs). This kind of problems is widely occurred in many scientific fields. For instance, the application fields range from parameter estimation in Partial Differential Equations to state estimation in Data Assimilation. Usually, the estimation process is performed by making use of Bayesian inference wherein errors associated to priors and observations are assumed to follow some known probability distribution. Since the estimation is based on a stochastic process, commonly, the posterior estimate is chosen to be the Maximum A Posteriori (MAP) estimate, this is, the sample from the posterior distribution which maximizes the posterior probability. Depending on the number of components (parameters) to be estimated and the quality of the prior sample, the posterior estimate can provide meaningful or meaningless information about the true set of parameters and their corresponding uncertainty. This is clear since the MAP is nothing but a sample from the posterior distribution and depending on its unknown bias, for instance, it can be possible to improve the predicted parameters by taking another sample from the posterior distribution. This is a particular case which opens the door to stochastic algorithms in order to improve the quality of predicted values based on Bayesian inferences.

Papers are welcome on all aspects of Combinatorial Optimization applied to Inverse Problems, including, but not restricted to:

• Markov Chain Monte Carlo (MCMC) methods for computing posterior estimates.

• Combinatorial optimization in Bayesian inference.

• Local Search methods in Data Assimilation.

• Meta-heuristics in Covariance Matrix estimation.

• Stochastic Methods for Uncertainty Quantification.

• Stochastic Algorithms for Network Design.

• Decision Support Systems based on historical data.

Important Dates:

December 30, 2016: Expression of interest (title and abstract to guest editors)

February 28, 2017: Full manuscript and cover letter

April 30, 2017: Review comments and decision

May 30, 2017: Revised, final manuscript

Paper Submission System:

EasyChair [ ]

Guest Editors:

Elias D. Nino-Ruiz, Ph.D.

Assistant Professor

Department of Computer Science

Universidad del Norte



Barranquilla, Colombia

Xinwei Deng, Ph.D.

Associate Professor

Department of Statistics

Virginia Tech



Blacksburg, VA 24060, USA

Ivan Saavedra Antolínez, Ph.D.

Director of Professional Services

Competitive Insight, LLC



Atlanta, GA 30080, USA

Yezid Donoso, Ph.D.

Associate Professor

Department of Computer Science

Universidad del los Andes



Bogota, Colombia


International Journal of Artificial Intelligence” does not want any publication fee for the International Journal of Artificial Intelligence.

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