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*'''Graphical Models'''  
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*'''Graphical Models, applications in agroecology'''  
**N. Peyrard, S. de Givry, A. Franc, S. Robin, R. Sabbadin, T. Schiex, M. Vignes, Exact and approximate inference in graphical models: variable elimination and beyond, [http://arxiv.org/abs/1506.08544 arXiv:1506.08544]  
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**B. Borgy, S. Gaba, N. Peyrard, R. Sabbadin, X. Reboud, Weeds dynamics buried in theseed bank : the use of hidden Markov model to predict life history traits. PlosOne, 2015<br>
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**N. Peyrard, S. de Givry, A. Franc, S. Robin, R. Sabbadin, T. Schiex, M. Vignes, Exact and approximate inference in graphical models: variable elimination and beyond, 2015, [http://arxiv.org/abs/1506.08544 arXiv:1506.08544]  
 
**A. Franc, M. Goulard, N. Peyrard, Chordal graphs to identify graphical models solutions of maximum of entropy under constraints on marginals, SIAM Discrete Mathematics, Vol. 24, N°3, 1104-1116, 2010.<br>
 
**A. Franc, M. Goulard, N. Peyrard, Chordal graphs to identify graphical models solutions of maximum of entropy under constraints on marginals, SIAM Discrete Mathematics, Vol. 24, N°3, 1104-1116, 2010.<br>
  
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*'''Static and adaptive spatial sampling for mapping '''  
 
*'''Static and adaptive spatial sampling for mapping '''  
**<div id="title">A. Albore, N. Peyrard, R. Sabbadin, F. Teichteil KönigsbuchAn, Online Replanning Approach for Crop Fields Mapping with Autonomous UAVs, ICAPS, 2015<br></div>  
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**<div id="title">A. Albore, N. Peyrard, R. Sabbadin, F. Teichteil KönigsbuchAn, Online Replanning Approach for Crop Fields Mapping with Autonomous UAVs, ICAPS, Israël, 2015<br></div>  
 
**M. Bonneau, S. Gaba, N. Peyrard, R. Sabbadin, Reinforcement learning-based design of sampling policies under cost constraints in Markov random fields: Application to weed maps reconstruction, Computational Statistics and Data Analysis, vol 72, 30-44, 2014  
 
**M. Bonneau, S. Gaba, N. Peyrard, R. Sabbadin, Reinforcement learning-based design of sampling policies under cost constraints in Markov random fields: Application to weed maps reconstruction, Computational Statistics and Data Analysis, vol 72, 30-44, 2014  
 
**M. Bonneau, N. Peyrard, R. Sabbadin, A Reinforcement-Learning Algorithm for Sampling Design in Markov Random Fields, ECAI, Montpellier, sept. 2012  
 
**M. Bonneau, N. Peyrard, R. Sabbadin, A Reinforcement-Learning Algorithm for Sampling Design in Markov Random Fields, ECAI, Montpellier, sept. 2012  
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*'''Graph-based Markov Decision Processes, applications in epidemiology and ecology'''  
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*'''Graph-based Markov Decision Processes, applications in agroecology'''  
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**J. Radoszycki, N. Peyrard, R. Sabbadin, Solving F3MDPs: collaborative multiagent Markov decision processes with factored transitions, rewards and stochastic policies, (Principle and Practices of Multi Agent systems PRIMA,&nbsp; Italy, 2015<br>
 
**J. Radoszycki, N. Peyrard, R. Sabbadin, Finding good stochastic factored policies for factored Markov decision processes, European Conference on Artificial Intelligence ECAI, Prague, Czech Republic, 2014  
 
**J. Radoszycki, N. Peyrard, R. Sabbadin, Finding good stochastic factored policies for factored Markov decision processes, European Conference on Artificial Intelligence ECAI, Prague, Czech Republic, 2014  
 
**P. Tixier, N. Peyrard, J.-N. Aubertot, S. Gaba, J. Radoszycki, G. Caron-Lormier, F. Vinatier, G. Mollot, R. Sabbadin, Modelling interaction networks for enhanced ecosystem services in agroecosystems, Advances in Ecological research, 49 437-480, 2013<br>  
 
**P. Tixier, N. Peyrard, J.-N. Aubertot, S. Gaba, J. Radoszycki, G. Caron-Lormier, F. Vinatier, G. Mollot, R. Sabbadin, Modelling interaction networks for enhanced ecosystem services in agroecosystems, Advances in Ecological research, 49 437-480, 2013<br>  
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**M. Choisy, N. Peyrard, R. Sabbadin, A probabilistic decision framework to optimise the dynamical evolution of a network: application to the control of childhood diseases, European Conference on Complex Systems (ECCS), Dresden - Germany, oct. 2007.  
 
**M. Choisy, N. Peyrard, R. Sabbadin, A probabilistic decision framework to optimise the dynamical evolution of a network: application to the control of childhood diseases, European Conference on Complex Systems (ECCS), Dresden - Germany, oct. 2007.  
 
**N. Peyrard and R. Sabbadin, Mean Field Approximation of the Policy Iteration Algorithm for Graph-based Markov Decision Processes, European Conference on Artificial Intelligence ECAI, Trentino -Italy, aug. 2006.
 
**N. Peyrard and R. Sabbadin, Mean Field Approximation of the Policy Iteration Algorithm for Graph-based Markov Decision Processes, European Conference on Artificial Intelligence ECAI, Trentino -Italy, aug. 2006.
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*'''Ecological network inference'''
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**&nbsp;C. Vacher, A. Tamaddoni-Nezhad, S. Kamenova, N. Peyrard, Y. Moalic, R. Sabbadin, L. Schwaller, J. Chiquet, M. Alex Smith, J. Vallance, V. Fievet, B. Jakuschkin, D. A. Bohan,&nbsp; Learning ecological networks from next generation sequencing data, Advances in Ecological Research,&nbsp; in Press<br>
  
 
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Version du 1 octobre 2015 à 08:53

Publications of Nathalie Peyrard


  • HDR : Méthodes variationnelles pour l'estimation, l'inférence et la décision dans les modèles graphiques, 2013. slides et manuscrit


  • Graphical Models, applications in agroecology
    • B. Borgy, S. Gaba, N. Peyrard, R. Sabbadin, X. Reboud, Weeds dynamics buried in theseed bank : the use of hidden Markov model to predict life history traits. PlosOne, 2015
    • N. Peyrard, S. de Givry, A. Franc, S. Robin, R. Sabbadin, T. Schiex, M. Vignes, Exact and approximate inference in graphical models: variable elimination and beyond, 2015, arXiv:1506.08544
    • A. Franc, M. Goulard, N. Peyrard, Chordal graphs to identify graphical models solutions of maximum of entropy under constraints on marginals, SIAM Discrete Mathematics, Vol. 24, N°3, 1104-1116, 2010.


  • Static and adaptive spatial sampling for mapping
    • A. Albore, N. Peyrard, R. Sabbadin, F. Teichteil KönigsbuchAn, Online Replanning Approach for Crop Fields Mapping with Autonomous UAVs, ICAPS, Israël, 2015
    • M. Bonneau, S. Gaba, N. Peyrard, R. Sabbadin, Reinforcement learning-based design of sampling policies under cost constraints in Markov random fields: Application to weed maps reconstruction, Computational Statistics and Data Analysis, vol 72, 30-44, 2014
    • M. Bonneau, N. Peyrard, R. Sabbadin, A Reinforcement-Learning Algorithm for Sampling Design in Markov Random Fields, ECAI, Montpellier, sept. 2012
    • N. Peyrard, R. Sabbadin, D. Spring, B. Brook, R. Mac Nally. Model-based adaptive spatial sampling for occurrence map construction, Statistics and Computing 23(1) 29-42, 2013. pdf
    • M. Bonneau, N. Peyrard, R. Sabbadin, Un cadre pour l'échantillonnage optimal dans les champs de Markov et un algorithme de résolution basé sur l'apprentissage par renforcement, JFPDA, Rouen, juin 2011.
    • N. Peyrard, R. Sabbadin, D. Spring, R. Mac Nally, B. Brook. Spatial sampling in HMRF mapping problems: static and adaptive algorithms, ECCS, Lisbon, Portugal, sept. 2010
    • N. Peyrard, R. Sabbadin, U. Farrokh Niaz, Decision-theoretic Optimal Sampling with Hidden Markov Random Fields, ECAI, Lisbon, Portugal, aug. 2010
    • M. Bonneau, N. Peyrard, R. Sabbadin, Echantillonnage spatial basé sur le krigeage pour la reconstruction de carte d'occurrence, RFIA, Caen, France, jan. 2010


  • Graph-based Markov Decision Processes, applications in agroecology
    • J. Radoszycki, N. Peyrard, R. Sabbadin, Solving F3MDPs: collaborative multiagent Markov decision processes with factored transitions, rewards and stochastic policies, (Principle and Practices of Multi Agent systems PRIMA,  Italy, 2015
    • J. Radoszycki, N. Peyrard, R. Sabbadin, Finding good stochastic factored policies for factored Markov decision processes, European Conference on Artificial Intelligence ECAI, Prague, Czech Republic, 2014
    • P. Tixier, N. Peyrard, J.-N. Aubertot, S. Gaba, J. Radoszycki, G. Caron-Lormier, F. Vinatier, G. Mollot, R. Sabbadin, Modelling interaction networks for enhanced ecosystem services in agroecosystems, Advances in Ecological research, 49 437-480, 2013
    • R. Sabbadin, N. Peyrard, N. Forsell, A framework and a mean-field algorithm for the local control of spatial processes,IJAR, 53(1) : 66-86, 2012. pdf
    • N. Peyrard, R. Sabbadin, E. Lo-Pelzer and J.-N. Aubertot, A Graph-based Markov Decision Process framework for Optimising Collective Management of Diseases in Agriculture: Application to Blackleg on Canola, International Congress on Modelling and Simulation (MODSIM), Christchurch, New Zeland, dec. 2007. pdf
    • M. Choisy, N. Peyrard, R. Sabbadin, A probabilistic decision framework to optimise the dynamical evolution of a network: application to the control of childhood diseases, European Conference on Complex Systems (ECCS), Dresden - Germany, oct. 2007.
    • N. Peyrard and R. Sabbadin, Mean Field Approximation of the Policy Iteration Algorithm for Graph-based Markov Decision Processes, European Conference on Artificial Intelligence ECAI, Trentino -Italy, aug. 2006.


  • Ecological network inference
    •  C. Vacher, A. Tamaddoni-Nezhad, S. Kamenova, N. Peyrard, Y. Moalic, R. Sabbadin, L. Schwaller, J. Chiquet, M. Alex Smith, J. Vallance, V. Fievet, B. Jakuschkin, D. A. Bohan,  Learning ecological networks from next generation sequencing data, Advances in Ecological Research,  in Press


  • Log Gaussian Cox Process
    • J. Radoszycki, N. Peyrard, R. Sabbadin, VBEM algorithm for the log Gaussian Cox process, Spatial Statistics conference, 2015


  • Contact process and variational methods, application to disease spread on networks
  • A. Franc, N. Peyrard, Rôle de la géométrie du réseau d'interaction dans l'émergence d'une maladie. Dans : Les maladies émergentes chez le végétal, l’animal et l’homme, Enjeux et stratégies d’analyse épidémiologique, Editions QUAE, 2010
  • A. Franc, N. Peyrard, B. Roche, Propagation d'agents pathogènes dans les réseaux. Dans : Introduction à l'épidémiologie quantitative des maladies infectieuses. Guégan J.F. and Choisy M. eds, 2009
  • N. Peyrard, U. Dieckmann, A. Franc, Long-range correlations improve understanding the influence of network structure on per contact dynamics, Theoretical Population Biology, Vol 73/3 pp 383-394, 2008
  • N. Peyrard and A. Franc, Cluster variation approximations for a contact process living on a graph, Physica A, vol. 358, pages 575-592 ,2005.
  • N. Peyrard and R. Sabbadin, Evaluation of the expected size of a SIR epidemics on a graph, UBIAT Resarch Report RR-2012-1, 2012 pdf


  • Permutation tests for disease spread analysis
    • N. Peyrard, F. Pellegrin, J. Chadoeuf and D. Nandris, Statistical analysis of the spatio-temporal dynamics of rubber Bark Necrosis: no evidence of pathogen transmission , Forest Pathology, (36), pages 360-371, 2006.
    • G. Thébaut, N. Peyrard, S. Dallot, A. Calonnec and G. Labonne, Investigating disease spread between two assessment dates with permutation tests on a lattice , Phytopathology, 95, pages 1453-1461, 2005.
    • N. Peyrard, A. Calonnec, F. Bonnot et J. Chadoeuf, Explorer un jeu de données sur grille par tests de permutation, Revue de Statistique Applique (RSA), 53(1), pages 59-78, 2005.


  • Model-based Image/Video Analysis
    • F. Forbes, N. Peyrard, C. Fraley, D. Georgian-Smith, D. Goldhaber, A. Raftery, Model-Based Region-Of-Interest Selection in Dynamic Breast MRI, Journal of Computer Assisted Tomography Decision, 30(4):675-687, 2006.
    • N. Peyrard, P. Bouthemy, Motion-based selection of relevant video segments for video summarization, Multimedia Tools and Applications journal, Special Issue, 26, pages 255-274, 2005.


  • HMRF for image segmentation
    • M. Charras-Garridoa, L. Azizia, F. Forbes, S. Doyle, N. Peyrard, D. Abrial, Joint estimation-classification framework for disease risk mapping International Journal of Applied Earth Observation and Geoinformation, 22:99-105, 2013
    • M. Charras-Garrido, D. Abrial, N. Peyrard, S. Dachian, New classification method for disease mapping based on discrete Hidden Markov Random Fields, Biostatistics, 13 : 241-255, 2012.
    • F. Forbes, N. Peyrard, Hidden Markov Models Selection Criteria based on Mean Field-like approximations, IEEE Trans. on PAMI, vol 25, n 9, pages 1089-1101, 2003.
    • G. Celeux, F. Forbes, N. Peyrard, EM Procedures Using Mean Field-Like Approximations for Markov Model-Based Image Segmentation, Pattern Recognition, vol 36, pages 131-144, 2003. pdf


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