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*HDR&nbsp;: Méthodes variationnelles pour l'estimation, l'inférence et la décision dans les modèles graphiques. [[Media:TalkHDR_NP.pdf|slides]] et [[Media:HdR_NP.pdf|manuscrit]]
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*'''HDR'''&nbsp;: Méthodes variationnelles pour l'estimation, l'inférence et la décision dans les modèles graphiques, 2013. [[Media:TalkHDR_NP.pdf|slides]] et [[Media:HdR_NP.pdf|manuscrit]]
  
*Graphical Models  
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*'''Graphical Models'''
 
**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.&nbsp;[[Media:073646R.pdf|pdf]]
 
**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.&nbsp;[[Media:073646R.pdf|pdf]]
  
*Static and adaptive spatial sampling for mapping  
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*'''Static and adaptive spatial sampling for mapping '''
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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, 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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**M. Bonneau, N. Peyrard, R. Sabbadin, Echantillonnage spatial basé sur le krigeage pour la reconstruction de carte d'occurrence, RFIA, Caen, France, jan. 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 epidemiology  
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**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
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*'''Graph-based Markov Decision Processes, applications in epidemiology'''
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**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  
 
**R. Sabbadin, N. Peyrard, N. Forsell, A framework and a mean-field algorithm for the local control of spatial processes,IJAR, 53(1)&nbsp;: 66-86, 2012. [[Media:IJAR_versionpubliee.pdf|pdf]]  
 
**R. Sabbadin, N. Peyrard, N. Forsell, A framework and a mean-field algorithm for the local control of spatial processes,IJAR, 53(1)&nbsp;: 66-86, 2012. [[Media:IJAR_versionpubliee.pdf|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. [[Media:PHOMA07.pdf|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. [[Media:PHOMA07.pdf|pdf]]  
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**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.
  
*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&nbsp;: 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&nbsp;: 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 [[Media:RR-2012-1.pdf|pdf]]
 
  
*Permutation tests for disease spread analysis  
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*'''Log Gaussian Cox Process'''
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**J. Radoszycki, N. Peyrard, R. Sabbadin, VBEM algorithm for the log Gaussian Cox process, Spatial Statistics conference, 2015<br>
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*'''Contact process and variational methods, application to disease spread on networks '''
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*A. Franc, N. Peyrard, Rôle de la géométrie du réseau d'interaction dans l'émergence d'une maladie. Dans&nbsp;: Les maladies émergentes chez le végétal, l’animal et l’homme, Enjeux et stratégies d’analyse épidémiologique, Editions QUAE, 2010
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*A. Franc, N. Peyrard, B. Roche, Propagation d'agents pathogènes dans les réseaux. Dans&nbsp;: 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 [[Media:RR-2012-1.pdf|pdf]]
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 +
 
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*'''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.  
 
**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.  
 
**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.
 
**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  
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*'''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.  
 
**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.
 
**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  
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*'''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-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&nbsp;: 241-255, 2012.  
 
**M. Charras-Garrido, D. Abrial, N. Peyrard, S. Dachian, New classification method for disease mapping based on discrete Hidden Markov Random Fields, Biostatistics, 13&nbsp;: 241-255, 2012.  

Version du 4 mai 2015 à 08:12

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
    • 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. pdf


  • 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, 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 epidemiology
    • 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
    • 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.


  • 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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