Nathalie Peyrard

Mathématiques et Informatique Appliquées

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Régis Sabbadin

Mathématiques et Informatique Appliquées

Marie‐Josée Cros

Mathématiques et Informatique Appliquées

Alain Franc

Centre d'Études Scientifiques et Techniques d'Aquitaine

Sabrina Gaba

Zone Atelier Moselle

Jean‐Noël Aubertot

AGroecologies, Innovations & Ruralities

Pierre‐Olivier Cheptou

Centre National de la Recherche Scientifique

Mathieu Bonneau

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Xavier Reboud

Agroécologie

Sam Nicol

Commonwealth Scientific and Industrial Research Organisation

Sebastian Le Coz

Mathématiques et Informatique Appliquées

Cooperative Institutions

Centre National de la Recherche Scientifique

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Institut National de la Recherche Agronomique

Institut national de recherche en informatique et en automatique

Département Mathématiques et Informatique Appliquées

Mathématiques et Informatique Appliquées

Université de Toulouse

Institut de Recherche en Informatique et Systèmes Aléatoires

Institut de Recherche pour le Développement

Institut National de la Recherche Agronomique du Niger

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A general method for estimating seed dormancy and colonisation in annual plants from the observation of existing flora

Ecology Letters (2018)

Matthieu Pluntz Sebastian Le Coz Nathalie Peyrard Roger Pradel Rémi Choquet

Abstract In plant ecology, characterising colonisation and extinction in plant metapopulations is challenging due to the non‐detectable seed bank that allows plants to emerge after several years of absence. In this study, we used a Hidden Markov Model to characterise seed dormancy, colonisation and germination solely from the presence–absence of standing flora. Applying the model to data from a long‐term survey of 38 annual weeds across France, we identified three homogeneous functional groups: (1) species persisting preferentially through spatial colonisation, (2) species persisting preferentially through seed dormancy and (3) a mix of both strategies. These groups are consistent with existing ecological knowledge, demonstrating that ecologically meaningful parameters can be estimated from simple presence–absence observations. These results indicate that such studies could contribute to the design of weed management strategies. They also open the possibility of testing life‐history theories such as the dormancy/colonisation trade‐off in natura .

Colonisation

Flora

Metapopulation

10.1111/ele.13097

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Citations (16)

Approximations de type champ moyen des modèles de champ de Markov pour la segmentation de données spatiales

Nathalie Peyrard

Ce travail s'inscrit dans la recherche actuelle en segmentation markovienne d'images. Cela concerne les problemes de selection de modele, d'estimation des parametres et de segmentation. Les methodes classiques pour traiter ces trois etapes ne peuvent etre appliquees directement dans le cas des champs de Markov caches du fait de la structure de dependance spatiale du modele. Des approximations sont necessaires. Une solution, assez couteuse, consiste a utiliser des simulations de Monte-Carlo. Nous proposons des methodes simples et moins lourdes, basees sur l'approximation en champ moyen d'un champ de Markov. L'interet de cette methode est de se ramener a un systeme de variables independantes, plus facile a manipuler. Dans un premier temps nous avons etudie, puis generalise le principe du champ moyen pour la mise en oeuvre de l'algorithme EM pour l'estimation des parametres du modele. Il en resulte une classe d'algorithmes, les algorithmes EM de type champ moyen, qui presentent l'avantage de prendre en cmpte la structure markovienne tout en ayant une simplicite de mise en oeuvre identique au cas independant. Parmi ces procedures, nous distinguons l'algorithme en champ simule, proche de l'algorithme Monte-Carlo EM(MCEM), en raison de ses performances en termes d'estimation et de segmentation dans le cas d'images simulees et reelles. Nous presentons ensuite des conditions suffisantes pour la convergence presque sure de l'algorithme MCEM, dans un contexte ou peu de resultats sont disponibles a cause de la complexite du modele markovien. Ces resultats sur l'estimation et la segmentation dans un cadre ou le nombre de classes du modele est connu sont completes par un travail sur l'etape de selection d'un modele markovien. La encore, l'outil champ moyen est interessant puisqu'il permet de definir des approximations simples de criteres classiques (Bayesian Information Criterion, Integrated Likelihood) et qui s'averent prometteuses en pratique.

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Spatial sampling in HMRF mapping problems: static and adaptive algorithms

HAL (Le Centre pour la Communication Scientifique Directe) (2010)

Nathalie Peyrard Régis Sabbadin Daniel Spring Ralph Mac Nally Barry W. Brook

Adaptive sampling

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Decision-theoretic Optimal Sampling in Hidden Markov Random Fields

Frontiers in artificial intelligence and applications (2010)

Nathalie Peyrard Régis Sabbadin Usman Niaz

Computation of the Most Probable Explanation (MPE) when probabilistic knowledge is expressed as a factored distribution is a classical AI reasoning problem: complete evidence is available about the values of some of the variables which are observed, and the problem consists in finding the most probable assignment of the remaining variables given the evidence. However, optimising the choice of the variables to observe (the sample) in order to maximise the MPE probability is a less classical and more difficult problem. In this article we tackle this question of optimal sampling in structured problems under limited budget, within the framework of Hidden Markov Random Fields (HMRF). The value of a sample (which we seek to optimise) is the expectation, over all possible sample outputs (observations), of the MPE probability. The contributions of this article are: i) an original probabilistic model for optimal sampling in HMRF ii) computational complexity results about this problem, leading in particular to approximability/inapproximability results and iii) an exact solution algorithm and two approximate solution algorithms of decreasing time complexity, which we empirically evaluate on a problem of spatial sampling for occurrence map restoration.

10.3233/978-1-60750-606-5-687

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What epidemiosurveillance brings or does not yet bring to the slightest dependence on plant protection products

HAL (Le Centre pour la Communication Scientifique Directe) (2017)

Xavier Reboud Alain Carpentier Jean‐Noël Aubertot Stéphane Lemarié Nathalie Peyrard

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A graph-based Markov decision process framework applied to the optimization of strategies for integrated management of diseases

HAL (Le Centre pour la Communication Scientifique Directe) (2007)

Nathalie Peyrard Régis Sabbadin Elise Pelzer Jean‐Noël Aubertot

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Content-Based Video Segmentation using Statistical Motion Models

Nathalie Peyrard Patrick Bouthémy

We present in this paper an original approach for content-based video segmentation using motion information. The method is generic and does not require any knowledge about the type of the processed video. Its relies on the analysis of the temporal evolution of the dynamic content of the video. The motion content is characterised by a probabilistic Gibbsian modelling of the distribution of local motion-related measurements. The designed statistical framework provides a well formalised similarity measure according to motion activity that we exploit to derive criteria for segmentation decision. Then, the considered merging criteria are sequentially applied between every two successive temporal units of the video to progressively form homogeneous segments in term of motion content. Experiments on real video documents demonstrate the ability of the proposed approach to provide a concise and meaningful overview of a video.

Motion analysis

Content (measure theory)

Similarity (geometry)

10.5244/c.16.51

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Chordal Graphs to Identify Graphical Model Solutions of Maximum of Entropy Under Constraints on Marginals

SIAM Journal on Discrete Mathematics (2010)

Alain Franc Goulard Michel Nathalie Peyrard

We consider the problem of specifying the joint distribution of a collection of variables with maximum entropy when a set of marginals are fixed. One can easily derive that the structure of the solution joint distribution is that of a graphical model. The potential functions are then marginals at some power. We address the following question, Under which conditions on the set of constraints is it possible to fully identify the canonical exponents in the maximum entropy solution as functions of the problem structure? Literature related to this topic is somewhat scattered in disciplines such as statistical mechanics, information theory, graph theory, and inference in graphical models. In this article we gather and link results from these different fields. From this, we show that for a particular class of constraints set on marginals, the chordal maximal coherent sets of constraints, it is possible to derive the canonical exponents of the graphical model solution of the maximum entropy problem as the numbers of occurrences of separators in an associated join tree. Conversely, we present sufficient conditions to ensure that a graphical model is a solution of a maximum entropy problem.

Graphical model

Kullback–Leibler divergence

Hidden variable theory

10.1137/080736466

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Reinforcement-learning for sampling design in Markov random fields

HAL (Le Centre pour la Communication Scientifique Directe) (2012)

Mathieu Bonneau Nathalie Peyrard Régis Sabbadin

Optimal sampling in spatial random fields is a complex problem, which mobilizes several research fields in spatial statistics and artificial intelligence. We consider the case where observations are discrete-valued and modelled by a Markov Random Field. Then we encode the sampling problem into the Markov Decision Process (MDP) framework. After exploring existing heuristic solutions as well as classical algorithms from the field of Reinforcement Learning (RL), we design an original algorithm, LSDP (Least Square Dynamic Programming), which uses simulated trajectories to solve approximately any finite-horizon MDP problem. Based on an empirical study of the behaviour of these different approaches on binary models, we derive the following conclusions: i) a naive heuristic, consisting in sampling sites where marginals are the most uncertain, is already an efficient sampling approach. ii) LSDP outperforms all the classical RL approaches we have tested. iii) LSDP outperforms the heuristic in cases when reconstruction errors have a high cost, or sampling actions are constrained. In addition, LSDP readily handles action costs in the optimisation problem, as well as cases when some sites of the MRF can not be observed.

Markov random field

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Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction

Theoretical Population Biology (2021)

Marie‐Josée Cros Jean‐Noël Aubertot Sabrina Gaba Xavier Reboud Régis Sabbadin

10.1016/j.tpb.2021.06.002

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Citations (6)