EVENEMENTS

• Journées Statistiques du sud, Toulouse, 19-21 juin 2024
• Colloque PMSMA sur les Processus Markoviens et semi-Markoviens et leurs Applications. Montpellier, 5-7 juin 2023

DOCTORANTS

• Mathieu Valdeyron ( 2024 - 2027). “Méthodes computationnelles pour des modèles de semi-Markov cachés avec effets mixtes – applications à des modèles de développement des plantes”. co-advised with Jean-Baptiste Durand (CIRAD, AMAP) and Sandra Plancade (MIAT).
• Hanna Bacave (2021 - 2024). “Nouveaux modèles de Semi Markov cachés multi-chaînes pour les dynamiques de métapopulation avec population partiellement observable”. co-advised with Pierre-Olivier Cheptou (CEFE-CNRS-Montpellier) and Nikolas Limnios (UTC).
• Anwar Abouabdallah (2019- 2022). “Apprentissage statistique pour l’identification d’OTUs et la caractérisation de la biodiversité”. co-advised with Alain Franc (BioGeCo - INRAE bordeaux) and Olivier Coulaud (INRIA Bordeaux).
• Etienne Auclair (2015-2018). “Methods for ecological network inference and management”. co-advised with Régis Sabbadin (MIAT).
• Sebastian Le Coz (2015-2018). “A hidden Markov model for weed species dynamics in an agricultural landscape”. co-advised with Pierre-Olivier Cheptou (CEFE-CNRS-Montpellier).
• Julia Radoszycki (2012-2015). “Methods for optimization-based design of management strategies of spatio-temporal processes: application ti the management of weed species”. co-advised with R. Sabbadin and S. Gaba
• Mathieu Bonneau (2009-2012). “Adaptive spatial sampling for reconstruction of weeds maps”, co-advised with R. Sabbadin and S. Gaba

PROJETS

• ANR project HSMM-INCA. HSMM: Inférence, Contrôle et Applications (01/2022-12/2025) (coordinatrice)
• ANR project AgroBiose. Biodiversité et services écosystémiques en agro-écosystèmes céréaliers intensifs (2014-2018)
• EcoPhyto 2018 project VESPA. Valeur et optimisation des dispositifs dépidémiosurveillance dans une stratégie durable de protection des cultures (2013-2016)
• MANAE. Managing Network in AgroEcology, joint INRA-CSIRO-University of Queensland projet (2013-2017) #- AIGM (ex MSTGA) network: Algorithmic Issues for Inference in Graphical Models (2006 -2019; funded by INRA MIA and RNSC/ISC Paris). This network gathers researchers from different disciplines (Statistics, Statistical Mechanics, Probability, Artificial Intellicence, Theoretical Epidemiology, Image Analysis)

LOGICIELS

• SEMMS: Spatial EM for Markovian Segmentation
• GMDP Toolbox : tools for approximate resolution of graph-based Markov decision processes

PUBLICATIONS PAR THEMES

HDR : Méthodes variationnelles pour l’estimation, l’inférence et la décision dans les modèles graphiques, 2013.

Book N. Peyrard, O. Gimenez (editors). Statistical approaches for hidden variables in Ecology. ISTE-Wiley, Serie Statistics and Ecology, 2022

Stochastic Block Model, applications to OTUs analysis

• M. A. Abouhabdallah, O. Coulaud, N. Peyrard, A. Franc, Computing WSBM marginals with Tensor-Train decomposition, submitted (preprint https://hal.inrae.fr/hal-04394024)
• M.-J. Cros, J.-M. Frigerio, N. Peyrard, A. Franc, Simple approaches for evaluation of OTU quality based on dissimilarity arrays, MBMG, 2024 (preprint https://hal.inrae.fr/BIOGECO/hal-03824588v1)
• M. A. Abouhabdallah, N. Peyrard, A. Franc, Does clustering of DNA barcodes agree with botanical classification directly at high taxonomic levels? Trees in French Guiana as a case study, Molecular Ecology Resources, 00, 1-16, 2022
• A. Franc, M. A. Abouhabdallah, N. Peyrard, Etudes avec le modèle SBM de la diversité présente dans un tableau de distances moléculaires, GDR EcoStat 2021
• A. Franc, with N. Peyrard, S. Tirera, B. de Thoisy, D. Donato and A. Lavergne, Statistical methods for ecoviromics of rodent reservoirs of zoonoses in French Guiana, vISEC, 2020
• M. A. Abouhabdallah, with O. Coulaud, A. Franc, N. Peyrard, Statistical learning for OTUs identification, vISEC, 2020

HMM and HSMM for Ecology

• H. Bacave, P.-O. Cheptou, N. Peyrard. Partially observable DBN to model the dynamics of partially observable metapopulations : advantages and open challenges, preprint (https://hal.inrae.fr/hal-04694779)
• H. Bacave, P.-O. Cheptou, N. Peyrard. Partially observable DBN to model the dynamics of partially observable metapopulations : advantages and open challenges, ISEC2024, Swansea 14-19 july 2024
• H. Bacave, P.-O. Cheptou, N. Limnios, N. Peyrard. HSMM piloté par les observations pour l’estimation de la dynamique des adventices. JDS2024, Bordeaux 27 - 31 may 2024
• H. Bacave, P.-O. Cheptou, N. Limnios, N. Peyrard. Non-parametric Observation Driven Hidden Markov Model, in revision, Communication in Statistics (preprint https://hal.inrae.fr/hal-04180133)
• S. Nicol, M.-J. Cros, N. Peyrard, R. Sabbadin, R. Trépos, R. A. Fuller, B. K. Woodworth. FlywayNet: A hidden semi-Markov model for inferring the structure of migratory bird networks from count data, Methods in Ecology and Evolution, 2022
• P.-O. Cheptou, S. Cordeau, S. Le Coz, N. Peyrard, Using coupled hidden Markov chains to estimate colonization and seed bank survival in a metapopulation of annual plants. Chapter in Statistical approaches for hidden variables in Ecology. ISTE-Wiley, Serie Statistics and Ecology, 2022
• N. Peyrard, with R. Sabbadin, M.-J. Cros, R. Trépos and S.Nicol, A hidden semi-Markov model for inferring the structure of migratory bird flyway networks, vISEC, 2020 / JDS 2021
• S. Le Coz, P.-O. Cheptou, N. Peyrard, A spatial Markovian framework for estimating regional and local dynamics of annual plants with dormant stage. Theoretical Population Biology, vol 127, pp 120 - 132, 2019
• S. Le Coz, P.-O. Cheptou, N. Peyrard, Multidimensional Hidden Markov Model with data feedback: new framework for estimating the dynamics of species with dormant stage. International Statistics and Ecology Conference, ISEC, 2018
• M. Pluntz, S. Le Coz, N. Peyrard, R. Pradel, R. Choquet, P.-O. Cheptou,A general method for estimating seed dormancy and colonization in annual plants from the observation of existing flora, Ecology Letters 21(7), 2018
• B. Borgy, S. Gaba, N. Peyrard, R. Sabbadin, X. Reboud, Weeds dynamics buried in the seed bank : the use of hidden Markov model to predict life history traits. PlosOne, 10(10) 2015

DBN structure inference, application to ecological network inference

• J. Chiquet, M.-J. Cros, M. Mariadassou, N. Peyrard, S. Robin. The Poisson Log-Normal model: a generic framework for analyzing joint abundance distributions. chapter in Statistical approaches for hidden variables in Ecology. ISTE-Wiley, Serie Statistics and Ecology, 2022
• N. Majdi et al, There’s no harm in having too much: A comprehensive toolbox of methods in trophic ecology, Food Webs, 2018 E. Auclair, N. Peyrard, R. Sabbadin, D. Bohan, Réseau bayésien dynamique étiqueté pour l’apprentissage de réseaux écologiques d’arthopodes dans les cultures, JFRB, Toulouse, 2018
• E. Auclair, N. Peyrard, R. Sabbadin, Labeled DBN learning with community structure knowledge, ECML, 2017
• E. Auclair, N. Peyrard, R. Sabbadin, Apprentissage de réseau bayésien dynamique étiqueté avec connaissance a priori sur la structure du réseau, 49ièmes journées de Statistique, 2017
• 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, 54 1-39, 2016

Decision theory and Conservation

• S. Nicol, J. Brazill-Boast, E. Gorrod, A. McSorley, N. Peyrard, and I. Chades. Quantifying the impact of uncertainty on threat management for biodiversity, Nature Communications, 2019
• H. Xiao, E. McDonald-Madden, R. Sabbadin, N. Peyrard, L. Dee, and I. Chades. The value of understanding feedbacks from ecosystem functions to species for managing ecosystems, Nature Communications, 2019
• H. Xiao , Dee, L. E., Chadès, I. , Peyrard, N. , Sabbadin, R. , Stringer, M. and McDonald‐Madden, E., Win‐wins for biodiversity and ecosystem service conservation depend on the trophic levels of the species providing services. J Appl Ecol., 2018
• S. Nicol, R. Sabbadin, N. Peyrard, I. Chadès, Finding the best management policy to eradicate invasive species from spatial ecological networks with simultaneous actions, Journal of Applied Ecology, 2017

Graphical Models

• N. Peyrard, M.-J. Cros, S. de Givry, A. Franc, S. Robin, R. Sabbadin, T. Schiex, M. Vignes, Exact or approximate inference in graphical models: why the choice is dictated by the treewidth, and how variable elimination can be exploited, Australian and NewZealand Journal of Statistics, vol 61(2), pp 89-133, 2019 (also on arxiv : [1])
• 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.

Spatial and spatio-temporal sampling for mapping and controling

• M.-J. Cros, J._N. Aubertot, S. Gaba, X. Reboud, R. Sabbadin, N. Peyrard, Improving pest monitoring network using a simulation-based approach to contribute to pesticide reduction, Theoretical Population Biology, 2021
• M. Bonneau, J. Martin, N. Peyrard, L. Rodgers, C. M. Romagosa and F. A. Johnson. Optimal spatial allocation of control effort to manage invasives in the face of imperfect detection and misclassification, Ecological Modelling, 2019
• M. Bonneau, N. Peyrard, S. Gaba, R. Sabbadin, Sampling for weed spatial distribution mapping need not be adaptive, Environmental and Ecological Statistics, 1(23), 2016 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

Factored Markov Decision Processes, applications in agroecology and epidemiology

• M.-J. Cros, J.-N. Auvertot, N. Peyrard and R. Sabbadin. GMDPtoolbox: a Matlab library for designing spatial management policies. Application to the long-term collective management of an airborne disease. PlosOne, 2017
• S. Gaba, N. Peyrard, J. Radoszycki and R. Sabbadin, A Markov Decision Process model to compare Ecosystem Services provided by agricultural landscapes, ieMSs 2016
• H. Xiao, R. Sabbadin, L. Dee, N. Peyrard, I. Chadès, E. McDonald-Madden, Can protection of ecosystem services preserve biodiversity? A novel approach combining decision optimization and network models, ESA meeting, 2016
• 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, chapter in Ecological Networks in an Agricultural World, Advance in Ecological Research, 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.

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-Garrido, 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, special issue of spatial Statistics, 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.