Littérature scientifique sur le sujet « Intensité de Papangelou »
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Articles de revues sur le sujet "Intensité de Papangelou"
Møller, Jesper, et Kasper K. Berthelsen. « Transforming Spatial Point Processes into Poisson Processes Using Random Superposition ». Advances in Applied Probability 44, no 1 (mars 2012) : 42–62. http://dx.doi.org/10.1239/aap/1331216644.
Texte intégralMøller, Jesper, et Kasper K. Berthelsen. « Transforming Spatial Point Processes into Poisson Processes Using Random Superposition ». Advances in Applied Probability 44, no 01 (mars 2012) : 42–62. http://dx.doi.org/10.1017/s0001867800005449.
Texte intégralTorrisi, Giovanni Luca. « Probability approximation of point processes with Papangelou conditional intensity ». Bernoulli 23, no 4A (novembre 2017) : 2210–56. http://dx.doi.org/10.3150/16-bej808.
Texte intégralPrivault, Nicolas. « Moments of k-hop counts in the random-connection model ». Journal of Applied Probability 56, no 4 (décembre 2019) : 1106–21. http://dx.doi.org/10.1017/jpr.2019.63.
Texte intégralMøller, Jesper, et Frederic Paik Schoenberg. « Thinning spatial point processes into Poisson processes ». Advances in Applied Probability 42, no 2 (juin 2010) : 347–58. http://dx.doi.org/10.1239/aap/1275055232.
Texte intégralMøller, Jesper, et Frederic Paik Schoenberg. « Thinning spatial point processes into Poisson processes ». Advances in Applied Probability 42, no 02 (juin 2010) : 347–58. http://dx.doi.org/10.1017/s0001867800004092.
Texte intégralHahn, Ute, Eva B. Vedel Jensen, Marie-Colette van Lieshout et Linda Stougaard Nielsen. « Inhomogeneous spatial point processes by location-dependent scaling ». Advances in Applied Probability 35, no 2 (juin 2003) : 319–36. http://dx.doi.org/10.1239/aap/1051201648.
Texte intégralHahn, Ute, Eva B. Vedel Jensen, Marie-Colette van Lieshout et Linda Stougaard Nielsen. « Inhomogeneous spatial point processes by location-dependent scaling ». Advances in Applied Probability 35, no 02 (juin 2003) : 319–36. http://dx.doi.org/10.1017/s0001867800012258.
Texte intégralCronie, Ottmar, Mehdi Moradi et Christophe A. N. Biscio. « A cross-validation-based statistical theory for point processes ». Biometrika, 27 juin 2023. http://dx.doi.org/10.1093/biomet/asad041.
Texte intégralPianoforte, Federico, et Riccardo Turin. « Multivariate Poisson and Poisson process approximations with applications to Bernoulli sums and -statistics ». Journal of Applied Probability, 30 septembre 2022, 1–18. http://dx.doi.org/10.1017/jpr.2022.33.
Texte intégralThèses sur le sujet "Intensité de Papangelou"
Flint, Ian. « Analyse stochastique de processus ponctuels : au-delà du processus de Poisson ». Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0085/document.
Texte intégralDeterminantal point processes have sparked interest in very diverse fields, such as random matrix theory, point process theory, and networking. In this manuscript, we consider them as random point processes, i.e. a stochastic collection of points in a general space. Hence, we are granted access to a wide variety of tools from point process theory, which allows for a precise study of many of their probabilistic properties. We begin with the study of determinantal point processes from an applicative point of view. To that end, we propose different methods for their simulation in a very general setting. Moreover, we bring to light a series of models derived from the well-known Ginibre point process, which are quite suited for applications. Thirdly, we introduce a differentiable gradient on the considered probability space. Thanks to some powerful tools from Dirichlet form theory, we discuss integration by parts for general point processes, and show the existence of the associated diffusion processes correctly associated to the point processes. We are able to apply these results to the specific case of determinantal point processes, which leads us to characterizing these diffusions in terms of stochastic differential equations. Lastly, we turn our attention to the difference gradient on the same space. In a certain sense, we define a Skohorod integral, which satisfies an integration by parts formula, i.e. its adjoint is the difference operator. An application to the study of a random transformation of the point process is given, wherein we characterize the distribution of the transformed point process under mild hypotheses
Flint, Ian. « Analyse stochastique de processus ponctuels : au-delà du processus de Poisson ». Electronic Thesis or Diss., Paris, ENST, 2013. http://www.theses.fr/2013ENST0085.
Texte intégralDeterminantal point processes have sparked interest in very diverse fields, such as random matrix theory, point process theory, and networking. In this manuscript, we consider them as random point processes, i.e. a stochastic collection of points in a general space. Hence, we are granted access to a wide variety of tools from point process theory, which allows for a precise study of many of their probabilistic properties. We begin with the study of determinantal point processes from an applicative point of view. To that end, we propose different methods for their simulation in a very general setting. Moreover, we bring to light a series of models derived from the well-known Ginibre point process, which are quite suited for applications. Thirdly, we introduce a differentiable gradient on the considered probability space. Thanks to some powerful tools from Dirichlet form theory, we discuss integration by parts for general point processes, and show the existence of the associated diffusion processes correctly associated to the point processes. We are able to apply these results to the specific case of determinantal point processes, which leads us to characterizing these diffusions in terms of stochastic differential equations. Lastly, we turn our attention to the difference gradient on the same space. In a certain sense, we define a Skohorod integral, which satisfies an integration by parts formula, i.e. its adjoint is the difference operator. An application to the study of a random transformation of the point process is given, wherein we characterize the distribution of the transformed point process under mild hypotheses
Vasseur, Aurélien. « Analyse asymptotique de processus ponctuels ». Electronic Thesis or Diss., Paris, ENST, 2017. http://www.theses.fr/2017ENST0062.
Texte intégralStein’s method constitutes one of the main techniques to solve some approximation problems in probability theory. In this manuscript, we apply it in the context of point processes. The first part of these investigations focuses on the Poisson point process. Its characteristic independence property provides a way to explain intuitively why a sequence of point processes becoming less and less repulsive can converge to such a point process. More generally, this leads to show some convergence results for some sequences of point processes built by several operations such as superposition, thinning and rescaling. The use of a distance on point processes, the so-called Kantorovich-Rubinstein distance, enables moreover the getting of some convergence rates. The second part is centered on a class of point processes with important attractiveness, called discrete α-stable point processes. Their structure based on a Poisson point process gives us a way to enlarge to these point processes the method used previously and to propose new results, via some properties that we state on these point processes
Maha, Petr. « Normální aproximace pro statistiku Gibbsových bodových procesů ». Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-372941.
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