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Relevant bibliographies by topics / Poisson superposition

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Journal articles

Academic literature on the topic 'Poisson superposition'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Poisson superposition"

1

Crane, Harry, and Peter Mccullagh. "Poisson superposition processes." Journal of Applied Probability 52, no. 4 (2015): 1013–27. http://dx.doi.org/10.1239/jap/1450802750.

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Superposition is a mapping on point configurations that sends the n-tuple into the n-point configuration , counted with multiplicity. It is an additive set operation such that the superposition of a k-point configuration in is a kn-point configuration in . A Poisson superposition process is the superposition in of a Poisson process in the space of finite-length -valued sequences. From properties of Poisson processes as well as some algebraic properties of formal power series, we obtain an explicit expression for the Janossy measure of Poisson superposition processes, and we study their law und

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2

Crane, Harry, and Peter Mccullagh. "Poisson superposition processes." Journal of Applied Probability 52, no. 04 (2015): 1013–27. http://dx.doi.org/10.1017/s0021900200113051.

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Abstract:

Superposition is a mapping on point configurations that sends the n-tuple into the n-point configuration , counted with multiplicity. It is an additive set operation such that the superposition of a k-point configuration in is a kn-point configuration in . A Poisson superposition process is the superposition in of a Poisson process in the space of finite-length -valued sequences. From properties of Poisson processes as well as some algebraic properties of formal power series, we obtain an explicit expression for the Janossy measure of Poisson superposition processes, and we study their law und

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3

Nagel, Werner, and Viola Weiss. "Limits of sequences of stationary planar tessellations." Advances in Applied Probability 35, no. 1 (2003): 123–38. http://dx.doi.org/10.1239/aap/1046366102.

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In order to increase the variety of feasible models for random stationary tessellations (mosaics), two operations acting on tessellations are studied: superposition and iteration (the latter is also referred to as nesting). The superposition of two planar tessellations is the superposition of the edges of the cells of both tessellations. The iteration of tessellations means that one tessellation is chosen as a ‘frame’ tessellation. The single cells of this ‘frame’ are simultaneously and independently subdivided by cut-outs of tessellations of an independent and identically distributed sequence

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4

Nagel, Werner, and Viola Weiss. "Limits of sequences of stationary planar tessellations." Advances in Applied Probability 35, no. 01 (2003): 123–38. http://dx.doi.org/10.1017/s0001867800012118.

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Abstract:

In order to increase the variety of feasible models for random stationary tessellations (mosaics), two operations acting on tessellations are studied: superposition and iteration (the latter is also referred to as nesting). The superposition of two planar tessellations is the superposition of the edges of the cells of both tessellations. The iteration of tessellations means that one tessellation is chosen as a ‘frame’ tessellation. The single cells of this ‘frame’ are simultaneously and independently subdivided by cut-outs of tessellations of an independent and identically distributed sequence

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5

Daribayev, Beimbet, Aksultan Mukhanbet, and Timur Imankulov. "Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators." Applied Sciences 13, no. 20 (2023): 11491. http://dx.doi.org/10.3390/app132011491.

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The Poisson equation is a fundamental equation of mathematical physics that describes the potential distribution in static fields. Solving the Poisson equation on a grid is computationally intensive and can be challenging for large grids. In recent years, quantum computing has emerged as a potential approach to solving the Poisson equation more efficiently. This article uses quantum algorithms, particularly the Harrow–Hassidim–Lloyd (HHL) algorithm, to solve the 2D Poisson equation. This algorithm can solve systems of equations faster than classical algorithms when the matrix A is sparse. The

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6

Møller, Jesper, and Kasper K. Berthelsen. "Transforming Spatial Point Processes into Poisson Processes Using Random Superposition." Advances in Applied Probability 44, no. 1 (2012): 42–62. http://dx.doi.org/10.1239/aap/1331216644.

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Most finite spatial point process models specified by a density are locally stable, implying that the Papangelou intensity is bounded by some integrable function β defined on the space for the points of the process. It is possible to superpose a locally stable spatial point process X with a complementary spatial point process Y to obtain a Poisson process X ⋃ Y with intensity function β. Underlying this is a bivariate spatial birth-death process (Xt, Yt) which converges towards the distribution of (X, Y). We study the joint distribution of X and Y, and their marginal and conditional distributi

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7

Møller, Jesper, and Kasper K. Berthelsen. "Transforming Spatial Point Processes into Poisson Processes Using Random Superposition." Advances in Applied Probability 44, no. 01 (2012): 42–62. http://dx.doi.org/10.1017/s0001867800005449.

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Abstract:

Most finite spatial point process models specified by a density are locally stable, implying that the Papangelou intensity is bounded by some integrable function β defined on the space for the points of the process. It is possible to superpose a locally stable spatial point process X with a complementary spatial point process Y to obtain a Poisson process X ⋃ Y with intensity function β. Underlying this is a bivariate spatial birth-death process (X t , Y t ) which converges towards the distribution of (X, Y). We study the joint distribution of X and Y, and their marginal and conditional distri

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8

Yang, Tae Young, and Lynn Kuo. "Bayesian computation for the superposition of nonhomogeneous poisson processes." Canadian Journal of Statistics 27, no. 3 (1999): 547–56. http://dx.doi.org/10.2307/3316110.

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9

Chen, Louis H. Y., and Aihua Xia. "Poisson process approximation for dependent superposition of point processes." Bernoulli 17, no. 2 (2011): 530–44. http://dx.doi.org/10.3150/10-bej290.

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10

Hegyi, S. "Scaling laws in hierarchical clustering models with Poisson superposition." Physics Letters B 327, no. 1-2 (1994): 171–78. http://dx.doi.org/10.1016/0370-2693(94)91546-6.

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