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Artykuły w czasopismach na temat "Intensité de Papangelou"
Møller, Jesper, i Kasper K. Berthelsen. "Transforming Spatial Point Processes into Poisson Processes Using Random Superposition". Advances in Applied Probability 44, nr 1 (marzec 2012): 42–62. http://dx.doi.org/10.1239/aap/1331216644.
Pełny tekst źródłaMøller, Jesper, i Kasper K. Berthelsen. "Transforming Spatial Point Processes into Poisson Processes Using Random Superposition". Advances in Applied Probability 44, nr 01 (marzec 2012): 42–62. http://dx.doi.org/10.1017/s0001867800005449.
Pełny tekst źródłaTorrisi, Giovanni Luca. "Probability approximation of point processes with Papangelou conditional intensity". Bernoulli 23, nr 4A (listopad 2017): 2210–56. http://dx.doi.org/10.3150/16-bej808.
Pełny tekst źródłaPrivault, Nicolas. "Moments of k-hop counts in the random-connection model". Journal of Applied Probability 56, nr 4 (grudzień 2019): 1106–21. http://dx.doi.org/10.1017/jpr.2019.63.
Pełny tekst źródłaMøller, Jesper, i Frederic Paik Schoenberg. "Thinning spatial point processes into Poisson processes". Advances in Applied Probability 42, nr 2 (czerwiec 2010): 347–58. http://dx.doi.org/10.1239/aap/1275055232.
Pełny tekst źródłaMøller, Jesper, i Frederic Paik Schoenberg. "Thinning spatial point processes into Poisson processes". Advances in Applied Probability 42, nr 02 (czerwiec 2010): 347–58. http://dx.doi.org/10.1017/s0001867800004092.
Pełny tekst źródłaHahn, Ute, Eva B. Vedel Jensen, Marie-Colette van Lieshout i Linda Stougaard Nielsen. "Inhomogeneous spatial point processes by location-dependent scaling". Advances in Applied Probability 35, nr 2 (czerwiec 2003): 319–36. http://dx.doi.org/10.1239/aap/1051201648.
Pełny tekst źródłaHahn, Ute, Eva B. Vedel Jensen, Marie-Colette van Lieshout i Linda Stougaard Nielsen. "Inhomogeneous spatial point processes by location-dependent scaling". Advances in Applied Probability 35, nr 02 (czerwiec 2003): 319–36. http://dx.doi.org/10.1017/s0001867800012258.
Pełny tekst źródłaCronie, Ottmar, Mehdi Moradi i Christophe A. N. Biscio. "A cross-validation-based statistical theory for point processes". Biometrika, 27.06.2023. http://dx.doi.org/10.1093/biomet/asad041.
Pełny tekst źródłaPianoforte, Federico, i Riccardo Turin. "Multivariate Poisson and Poisson process approximations with applications to Bernoulli sums and -statistics". Journal of Applied Probability, 30.09.2022, 1–18. http://dx.doi.org/10.1017/jpr.2022.33.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaDeterminantal 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.
Pełny tekst źródłaDeterminantal 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.
Pełny tekst źródłaStein’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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