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Start Over Descriptor "METHODE MCMC" Topic 01 natural sciences
3 results on '"METHODE MCMC"'

1. A continuous time-and-state epidemic model fitted to ordinal categorical data observed on a lattice at discrete times

Author

Rémi Crété, Valérie Caffier, Besnik Pumo, Samuel Soubeyrand, Frédérique Didelot, Laboratoire Angevin de Recherche en Mathématiques (LAREMA), Université d'Angers (UA)-Centre National de la Recherche Scientifique (CNRS), Biostatistique et Processus Spatiaux (BIOSP), Institut National de la Recherche Agronomique (INRA), INH : Pathologie végétale : biodiversité, écologie, interactions bioagresseurs-plantes (PAVE), Institut National de la Recherche Agronomique (INRA)-Université d'Angers (UA), This work was supported by MODEMAVE research project from the Région Pays de la Loire., Biostatistique et Processus Spatiaux (BioSP), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Statistics, Statistical and Compter Science Department, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, MODEMAVE research project from the Region Pays de la Loire, Crété, Rémi, and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)-Université d'Angers (UA)

Subjects
0106 biological sciences, statistique spatiale, 01 natural sciences, paramètre, 010104 statistics & probability, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Lattice (order), Statistics, simulation de monte carlo, [STAT.CO]Statistics [stat]/Computation [stat.CO], General Environmental Science, Mathematics, lattice, Calcul, Applied Mathematics, méthode mcmc, facteur climatique, Regular lattice, inférence bayésienne, Agricultural and Biological Sciences (miscellaneous), symbols, chaîne de markov, Statistics, Probability and Uncertainty, General Agricultural and Biological Sciences, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Statistics and Probability, Méthodologie, modèle spatiotemporel, modèle à temps continu, processus spatiotemporel, Bayesian inference, inférence statistique, procédé biologique, symbols.namesake, Applied mathematics, 0101 mathematics, Categorical variable, point process, Methodology, statistique bayésienne, Markov chain Monte Carlo, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], approche bayésienne, modèle bayésien, analyse multivariable, Computation, Bayesian framework, Epidemic model, tavelure du pommier, 010606 plant biology & botany
Abstract

We consider a spatio-temporal model to describe the spread of apple scab within an orchard composed of several plots. The model is defined on a regular lattice and evolves in continuous time. Based on ordinal categorical data observed only at some discrete instants, we adopt a continuous-time approach and apply a Bayesian framework for estimating unknown parameters.

Published
2012
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2. Inferring weed spatial distribution from multi-type data

Author

S. Soubeyrand, A. Bourgeois, Nathalie G. Munier-Jolain, Benjamin Borgy, Sabrina Gaba, Pascal Monestiez, Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Biologie et Gestion des Adventices (BGA), Université de Bourgogne (UB)-Institut National de la Recherche Agronomique (INRA)-Etablissement National d'Enseignement Supérieur Agronomique de Dijon (ENESAD), ANR grant STRA-08-02, and Soubeyrand, Samuel

Subjects
cartographie spatiale, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Méthodologie, weed mapping, Computer science, analyse mathématique, Context (language use), weed patch, log Gaussian Cox process, Bayesian inference, 01 natural sciences, Data type, Multivariate interpolation, 010104 statistics & probability, processus de cox, mauvaise herbe, Econometrics, végétation des parcelles, Bayesian hierarchical modeling, processus de gauss poisson, 0101 mathematics, [STAT.CO]Statistics [stat]/Computation [stat.CO], Bayesian hierarchical model, Calcul, 2. Zero hunger, Ecological Modeling, Methodology, méthode mcmc, distribution spatiale, Sampling (statistics), 04 agricultural and veterinary sciences, 15. Life on land, cartographie de la végétation, Agricultural sciences, modèle bayésien, Computer Science::Multiagent Systems, interpolation spatiale, cartographie à la parcelle, Computation, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, spatial interpolation, Weed, Biological system, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Sciences agricoles, processus gaussien, Interpolation
Abstract

International audience; An accurate weed management in a context of sustainable agriculture relies on the knowledge about spatial weed distribution within fields. To improve the representation of patchy spatial distributions of weeds, several sampling strategies are used and lead to various weed measurements (abundance, count, patch boundaries). Here, we propose a hierarchical Bayesian model which includes such multitype data and which allows the interpolation of weed spatial distributions (using a MCMC algorithm). The weed pattern is modeled with a log Gaussian Cox process and the various weed measurements are modeled with different observation processes. The application of the method to simulated data shows the advantage of combining several types of data (instead of using only one type of data). The method is also applied to infer the weed spatial distribution for real data.

Published
2012
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3. Statistical analysis of extreme events in a nonstationary context via a Bayesian framework. Case study with peak-over-threshold data

Author

P. Bois, Benjamin Renard, Michel Lang, Hydrologie-Hydraulique (UR HHLY), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
Environmental Engineering, Stationary process, 010504 meteorology & atmospheric sciences, [SDE.MCG]Environmental Sciences/Global Changes, Bayesian probability, CLIMATE CHANGE, 0207 environmental engineering, BAYESIAN ANALYSIS, 02 engineering and technology, 01 natural sciences, UNCERTAINTIES, symbols.namesake, Gumbel distribution, INCERTITUDE, STATISTIQUE BAYESIENNE, Generalized Pareto distribution, STATIONARITY, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], EXTREME EVENTS, METHODE MCMC, Econometrics, Environmental Chemistry, Applied mathematics, 020701 environmental engineering, Safety, Risk, Reliability and Quality, Extreme value theory, ANALYSE FREQUENTIELLE, 0105 earth and related environmental sciences, General Environmental Science, Water Science and Technology, Mathematics, ANALYSE STATISTIQUE, FREQUENCY ANALYSIS, Markov chain Monte Carlo, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], THEORIE DES VALEURS EXTREMES, STATIONNARITE, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, CHANGEMENT CLIMATIQUE, symbols, Generalized extreme value distribution, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], Quantile
Abstract

Statistical analysis of extremes currently assumes that data arise from a stationary process, although such an hypothesis is not easily assessable and should therefore be considered as an uncertainty. The aim of this paper is to describe a Bayesian framework for this purpose, considering several probabilistic models (stationary, step-change and linear trend models) and four extreme values distributions (exponential, generalized Pareto, Gumbel and GEV). Prior distributions are specified by using regional prior knowledge about quantiles. Posterior distributions are used to estimate parameters, quantify the probability of models and derive a realistic frequency analysis, which takes into account estimation, distribution and stationarity uncertainties. MCMC methods are needed for this purpose, and are described in the article. Finally, an application to a POT discharge series is presented, with an analysis of both occurrence process and peak distribution.

Published
2006
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