Results 281 to 290 of about 400,590 (330)
Some of the next articles are maybe not open access.
2014
This chapter will serve as a quick review of the essential results in the theory of Poisson point processes (PPPs) that we shall use for our analysis later in the book. However, we shall first take a step back and discuss point processes in general and their applicability to our wireless deployments of interest.
Alexander Drewitz +2 more
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This chapter will serve as a quick review of the essential results in the theory of Poisson point processes (PPPs) that we shall use for our analysis later in the book. However, we shall first take a step back and discuss point processes in general and their applicability to our wireless deployments of interest.
Alexander Drewitz +2 more
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Poisson branching point processes
Journal of Mathematical Physics, 1984We investigate the statistical properties of a special branching point process. The initial process is assumed to be a homogeneous Poisson point process (HPP). The initiating events at each branching stage are carried forward to the following stage.
Kuniaki Matsuo +2 more
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Robustness for Inhomogeneous Poisson Point Processes
Annals of the Institute of Statistical Mathematics, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Assunção, Renato, Guttorp, Peter
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Time series with Poisson point process
Applied Mathematics and Computation, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ghazal, M. A., Aly, A. Mitwalli
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Point processes subordinated to compound Poisson processes
Theory of Probability and Mathematical Statistics, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kobylych, K. V., Sakhno, L. M.
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Poisson point and Poisson processes
2017This chapter starts with a general description of Poisson point processes. These processes are defined from four natural axioms describing the spatial distribution of so-called Poisson points scattered homogeneously in a random manner across the d-dimensional Euclidean space.
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Compressive Data Aggregation from Poisson point process observations
2015 International Symposium on Wireless Communication Systems (ISWCS), 2015This paper introduces Stochastic Compressive Data Aggrega The Poisson point process (PPP) models the random deployment, and at the same time, allows the efficient implementation of an adequate sparsifying matrix, the random discrete Fourier transform (RDFT). The signal recovery is based on the RDFT which reveals the frequency content of smooth signals,
Pastor, Giancarlo +4 more
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2015
Throughout this note we will use the following notations. An interval of the type [l, r), \(-\infty < l < r \le \infty \) is called a time interval and is denoted by \(T, T_1, T_2, \ldots \). T is regarded as a measurable space associated with the topological \(\sigma \)-algebra on \(\mathscr {T}\) on T.
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Throughout this note we will use the following notations. An interval of the type [l, r), \(-\infty < l < r \le \infty \) is called a time interval and is denoted by \(T, T_1, T_2, \ldots \). T is regarded as a measurable space associated with the topological \(\sigma \)-algebra on \(\mathscr {T}\) on T.
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2014
Poisson point processes can be used as a cornerstone in the construction of very different stochastic objects such as, for example, infinitely divisible distributions, Markov processes with complex dynamics, objects of stochastic geometry and so forth.
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Poisson point processes can be used as a cornerstone in the construction of very different stochastic objects such as, for example, infinitely divisible distributions, Markov processes with complex dynamics, objects of stochastic geometry and so forth.
openaire +1 more source