Nathalie Peyrard
Nathalie Peyrard
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Cited by
Cited by
EM procedures using mean field-like approximations for Markov model-based image segmentation
G Celeux, F Forbes, N Peyrard
Pattern recognition 36 (1), 131-144, 2003
Hidden Markov random field model selection criteria based on mean field-like approximations
F Forbes, N Peyrard
IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (9), 1089-1101, 2003
Learning ecological networks from next-generation sequencing data
C Vacher, A Tamaddoni-Nezhad, S Kamenova, N Peyrard, Y Moalic, ...
Advances in ecological research 54, 1-39, 2016
There's no harm in having too much: a comprehensive toolbox of methods in trophic ecology
N Majdi, N Hette-Tronquart, E Auclair, A Bec, T Chouvelon, B Cognie, ...
Food webs 17, e00100, 2018
Modelling interaction networks for enhanced ecosystem services in agroecosystems
P Tixier, N Peyrard, JN Aubertot, S Gaba, J Radoszycki, G Caron-Lormier, ...
Advances in ecological research 49, 437-480, 2013
A framework and a mean-field algorithm for the local control of spatial processes
R Sabbadin, N Peyrard, N Forsell
International Journal of Approximate Reasoning 53 (1), 66-86, 2012
Classification method for disease risk mapping based on discrete hidden Markov random fields
M Charras-Garrido, D Abrial, JD Goër, S Dachian, N Peyrard
Biostatistics 13 (2), 241-255, 2012
Quantifying the impact of uncertainty on threat management for biodiversity
S Nicol, J Brazill-Boast, E Gorrod, A McSorley, N Peyrard, I Chadès
Nature Communications 10 (1), 3570, 2019
Motion-based selection of relevant video segments for video summarization
N Peyrard, P Bouthemy
Multimedia Tools and Applications 26, 259-276, 2005
Dynamics of weeds in the soil seed bank: a hidden Markov model to estimate life history traits from standing plant time series
B Borgy, X Reboud, N Peyrard, R Sabbadin, S Gaba
PloS one 10 (10), e0139278, 2015
Model-based adaptive spatial sampling for occurrence map construction
N Peyrard, R Sabbadin, D Spring, B Brook, R Mac Nally
Statistics and Computing 23, 29-42, 2013
Win‐wins for biodiversity and ecosystem service conservation depend on the trophic levels of the species providing services
H Xiao, LE Dee, I Chadès, N Peyrard, R Sabbadin, M Stringer, ...
Journal of Applied Ecology 55 (5), 2160-2170, 2018
Mean field approximation of the policy iteration algorithm for graph-based Markov decision processes
N Peyrard, R Sabbadin
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on …, 2006
Explorer un jeu de données sur grille par tests de permutation
N Peyrard, A Calonnec, F Bonnot, J Chadoeuf
Revue de Statistique Appliquée 53 (1), 59-78, 2005
The value of understanding feedbacks from ecosystem functions to species for managing ecosystems
H Xiao, E McDonald-Madden, R Sabbadin, N Peyrard, LE Dee, I Chadès
Nature Communications 10 (1), 3901, 2019
Model-based region-of-interest selection in dynamic breast MRI
F Forbes, N Peyrard, C Fraley, D Georgian-Smith, DM Goldhaber, ...
Journal of Computer Assisted Tomography 30 (4), 675-687, 2006
Approximations de type champ moyen des modèles de champ de Markov pour la segmentation de données spatiales
N Peyrard
Université Joseph Fourier (Grenoble), 2001
A general method for estimating seed dormancy and colonisation in annual plants from the observation of existing flora
M Pluntz, SL Coz, N Peyrard, R Pradel, R Choquet, PO Cheptou
Ecology letters 21 (9), 1311-1318, 2018
Reinforcement learning-based design of sampling policies under cost constraints in Markov random fields: Application to weed map reconstruction
M Bonneau, S Gaba, N Peyrard, R Sabbadin
Computational Statistics & Data Analysis 72, 30-44, 2014
Long-range correlations improve understanding of the influence of network structure on contact dynamics
N Peyrard, U Dieckmann, A Franc
Theoretical Population Biology 73 (3), 383-394, 2008
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