Relevant bibliographies by topics / Geometrical Probability

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Geometrical Probability'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Geometrical Probability"

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Irvine, Richard. "A Geometrical Approach to Conflict Probability Estimation." Air Traffic Control Quarterly 10, no. 2 (2002): 85–113. http://dx.doi.org/10.2514/atcq.10.2.85.

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Avram, Florin, and Dimitris Bertsimas. "On Central Limit Theorems in Geometrical Probability." Annals of Applied Probability 3, no. 4 (1993): 1033–46. http://dx.doi.org/10.1214/aoap/1177005271.

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Ciaglia, Florio Maria, Alberto Ibort, and Giuseppe Marmo. "Geometrical structures for classical and quantum probability spaces." International Journal of Quantum Information 15, no. 08 (2017): 1740007. http://dx.doi.org/10.1142/s021974991740007x.

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On the affine space containing the space [Formula: see text] of quantum states of finite-dimensional systems, there are contravariant tensor fields by means of which it is possible to define Hamiltonian and gradient vector fields encoding the relevant geometrical properties of [Formula: see text]. Guided by Dirac’s analogy principle, we will use them as inspiration to define contravariant tensor fields, Hamiltonian and gradient vector fields on the affine space containing the space of fair probability distributions on a finite sample space and analyze their geometrical properties. Most of our
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Jia, W., and J. Wang. "Analysis of connectivity for sensor networks using geometrical probability." IEE Proceedings - Communications 153, no. 2 (2006): 305. http://dx.doi.org/10.1049/ip-com:20045176.

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Lindsay, Bruce G., Robert E. Kass, and Paul W. Vos. "Geometrical Foundations of Asymptotic Inference." Journal of the American Statistical Association 94, no. 446 (1999): 646. http://dx.doi.org/10.2307/2670184.

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Gzyl, H., and F. Nielsen. "Geometry of the probability simplex and its connection to the maximum entropy method." Journal of Applied Mathematics, Statistics and Informatics 16, no. 1 (2020): 25–35. http://dx.doi.org/10.2478/jamsi-2020-0003.

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AbstractThe use of geometrical methods in statistics has a long and rich history highlighting many different aspects. These methods are usually based on a Riemannian structure defined on the space of parameters that characterize a family of probabilities. In this paper, we consider the finite dimensional case but the basic ideas can be extended similarly to the infinite-dimensional case. Our aim is to understand exponential families of probabilities on a finite set from an intrinsic geometrical point of view and not through the parameters that characterize some given family of probabilities.Fo
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Mikov, Alexander. "Geometrical digraphs as models of heterogeneous computer networks." Informatization and communication 4 (November 2020): 50–59. http://dx.doi.org/10.34219/2078-8320-2020-11-4-50-59.

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New mathematical models of networks are considered — geometrical oriented graphs. Such models adequately reflect the structure of heterogeneous wireless computer, sensor and other networks. The strongly connected components of geometric digraphs, their dependence on the number of network nodes in a given area, on the distributions of the radii of the zones of reception and transmission of signals are investigated. The probabilistic characteristics of random geometric digraphs, the features of the dependences of the probability of strong connectivity on the number of nodes in the digraphs for v
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Itoh, Mitsuhiro, and Hiroyasu Satoh. "Information geometry of Busemann-barycenter for probability measures." International Journal of Mathematics 26, no. 06 (2015): 1541007. http://dx.doi.org/10.1142/s0129167x15410074.

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Using Busemann function of an Hadamard manifold X we define the barycenter map from the space 𝒫+(∂X, dθ) of probability measures having positive density on the ideal boundary ∂X to X. The space 𝒫+(∂X, dθ) admits geometrically a fiber space structure over X from Fisher information geometry. Following the arguments in [E. Douady and C. Earle, Conformally natural extension of homeomorphisms of the circle, Acta Math.157 (1986) 23–48; G. Besson, G. Courtois and S. Gallot, Entropies et rigidités des espaces localement symétriques de coubure strictement négative, Geom. Funct. Anal.5 (1995) 731–799; M
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Krapivsky, P. L., L. I. Nazarov, and M. V. Tamm. "Geometrical selection in growing needles." Journal of Statistical Mechanics: Theory and Experiment 2019, no. 7 (2019): 073206. http://dx.doi.org/10.1088/1742-5468/ab270c.

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Azaïs, Jean-Marc, José R. León, and Joaquín Ortega. "Geometrical characteristics of Gaussian sea waves." Journal of Applied Probability 42, no. 2 (2005): 407–25. http://dx.doi.org/10.1239/jap/1118777179.

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In this work, we study some geometrical properties of a stationary Gaussian field modeling the sea surface, using the energy spectrum. We consider the length of a crest and the mean speed of contours, which can be expressed as integrals over level sets. We also give central limit theorems for some of these quantities, using chaos expansions.
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Dissertations / Theses on the topic "Geometrical Probability"

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Avram, Florin, and Dimitris J. Bertsimas. "The Minimum Spanning Tree Constant in Geometrical Probability and Under the Independent Model; A Unified Approach." Massachusetts Institute of Technology, Operations Research Center, 1990. http://hdl.handle.net/1721.1/5189.

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Given n uniformly and independently points in the d dimensional cube of unit volume, it is well established that the length of the minimum spanning tree on these n points is asymptotic to /3MsT(d)n(d-l)/d,where the constant PMST(d) depends only on the dimension d. It has been a major open problem to determine the constant 3MST(d). In this paper we obtain an exact expression of the constant MST(d) as a series expansion. Truncating the expansion after a finite number of terms yields a sequence of lower bounds; the first 3 terms give a lower bound which is already very close to the empirically es
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Cecchi, Bernales Paulina Alejandra. "Invariant measures in symbolic dynamics : a topological, combinatorial and geometrical approach." Thesis, Sorbonne Paris Cité, 2019. https://theses.md.univ-paris-diderot.fr/CECCHI-BERNALES_Paulina_2_complete_20190626.pdf.

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Dans ce travail nous étudions quelques propriétés des systèmes symboliques, avec un accent particulier mis sur le rôle joué par les mesures invariantes de tels systèmes. Nous nous attachons à l'étude des mesures invariantes d'un point de vue topologique, combinatoire et géométrique. Du point de vue topologique, nous nous concentrons sur le problème de l'équivalence orbitale et l'équivalence orbitale forte entre des systèmes dynamiques donnés par des actions minimales de Z, par l'étude d'un invariant algébrique, à savoir, le groupe de dimension dynamique. Notre travail donne une description du
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Silva, AntÃnio Klinger GuedÃlha da. "Probabilidade geomÃtrica: generalizaÃÃes do problema da agulha de Buffon e aplicaÃÃes." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11715.

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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior<br>O presente trabalho tem por finalidades: demonstrar o problema da agulha de Buffon, fazer uma pequena generalizaÃÃo do resultado obtido e apresentar aplicaÃÃes baseadas nos fundamentos do referido problema. O problema da agulha de Buffon està inserido no estudo da Teoria das Probabilidades, particularmente na subÃrea de probabilidade geomÃtrica. Para chegarmos à soluÃÃo desta questÃo, alÃm dos conceitos e propriedades atinentes à Teoria das probabilidades à necessÃrio o conhecimento de noÃÃes bÃsicas do cÃlculo integral. Nos capÃtu
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Presles, Benoît. "Caractérisation géométrique et morphométrique 3-D par analyse d'image 2-D de distributions dynamiques de particules convexes anisotropes. Application aux processus de cristallisation." Thesis, Saint-Etienne, EMSE, 2011. http://www.theses.fr/2011EMSE0632/document.

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La cristallisation en solution est un procédé largement utilisé dans l'industrie comme opération de séparation et de purification qui a pour but de produire des solides avec des propriétés spécifiques. Les propriétés concernant la taille et la forme ont un impact considérable sur la qualité finale des produits. Il est donc primordial de pouvoir déterminer la distribution granulométrique (DG) des cristaux en formation. En utilisant une caméra in situ, il est possible de visualiser en temps réel les projections 2D des particules 3D présentes dans la suspension. La projection d'un objet 3D sur un
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Pereira, Carlos André Bogéa. "Alguns tópicos em probabilidade geométrica." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306582.

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Orientador: Simão Nicolau Stelmastchuk<br>Dissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica<br>Made available in DSpace on 2018-08-17T21:15:56Z (GMT). No. of bitstreams: 1 Pereira_CarlosAndreBogea_M.pdf: 2160729 bytes, checksum: f46cc601d486b802f179e9fd8befb099 (MD5) Previous issue date: 2011<br>Resumo: Ao nosso entender, a Probabilidade Geométrica quantifica a probabilidade de ocorrer alguns fenômenos associados a entes geométricos. O primeiro estudo, talvez o mais famoso, a ser realizado neste sentido é o p
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Lindsay, Larry J. "Quantization Dimension for Probability Definitions." Thesis, University of North Texas, 2001. https://digital.library.unt.edu/ark:/67531/metadc3008/.

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The term quantization refers to the process of estimating a given probability by a discrete probability supported on a finite set. The quantization dimension Dr of a probability is related to the asymptotic rate at which the expected distance (raised to the rth power) to the support of the quantized version of the probability goes to zero as the size of the support is allowed to go to infinity. This assumes that the quantized versions are in some sense ``optimal'' in that the expected distances have been minimized. In this dissertation we give a short history of quantization as well as some ba
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Charmoy, Philippe H. A. "On the geometric and analytic properties of some random fractals." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:a9bbee2c-9958-464a-ac9e-bb8c42e107ea.

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The heat content of a domain D of &Ropf;<sup>d</sup> is defined as</sp> < p >E(s) = &int;<sub>D</sub> u(s,x)dx, where u is the solution to the heat equation with zero initial condition and unit Dirichlet boundary condition. This thesis studies the behaviour of E(s) for small s with a particular emphasis on the case where $D$ is a planar domain whose boundary is a random Koch curve. When &part;D is spatially homogeneous, we show that we can recover the upper and lower Minkowski dimensions of &part;D from E(s). Furthermore, in some cases where the Minkowski dimension does exist, finer fluctuatio
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Ma, Kin-Keung. "Infinite prefix codes for geometric distributions /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20MA.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 74-76). Also available in electronic version. Access restricted to campus users.
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Saive, Yannick. "DirCNN: Rotation Invariant Geometric Deep Learning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252573.

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Recently geometric deep learning introduced a new way for machine learning algorithms to tackle point cloud data in its raw form. Pioneers like PointNet and many architectures building on top of its success realize the importance of invariance to initial data transformations. These include shifting, scaling and rotating the point cloud in 3D space. Similarly to our desire for image classifying machine learning models to classify an upside down dog as a dog, we wish geometric deep learning models to succeed on transformed data. As such, many models employ an initial data transform in their mode
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Ho, Pak-kei. "Parametric and non-parametric inference for Geometric Process." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31483859.

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Books on the topic "Geometrical Probability"

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Djang, Fred C. Applications of geometrical probability. Consortium for Mathematics and Its Applications, Inc. (COMAP), 1988.

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Djang, Fred C. Applications of geometrical probability. COMAR, 1988.

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Klain, Daniel A. Introduction to geometric probability. Cambridge University Press, 1997.

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Avram, Florin. On central limit theorems in geometrical probability. Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1991.

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Mathai, A. M. An introduction to geometrical probability: Distributional aspects with applications. Gordon & Breach, 1999.

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Fernique, Xavier, Bernard Heinkel, Paul-André Meyer, and Michael B. Marcus, eds. Geometrical and Statistical Aspects of Probability in Banach Spaces. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/bfb0077094.

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Ghandehari, Mostafa. Tennis, geometric progression, probability and basketball. University of Texas at Arlington, Dept. of Mathematics, 1999.

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Ambartzumian, R. V. Factorization calculus and geometric probability. Cambridge University Press, 1990.

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Integral geometry and geometric probability. 2nd ed. Cambridge University Press, 2004.

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Kalashnikov, Vladimir Vi͡acheslavovich. Geometric sums, bounds for rare events with applications: Risk analysis, reliability, queueing. Kluwer Academic, 1997.

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Book chapters on the topic "Geometrical Probability"

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Zhang, Junping, Stan Z. Li, and Jue Wang. "Geometrical Probability Covering Algorithm." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_29.

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Zacks, Shelemyahu. "Geometrical Probability, Coverage and Visibility In Random Fields." In Stochastic Visibility in Random Fields. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2690-1_3.

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Jia, Weijia, Yingjie Fu, and Jianxin Wang. "Analysis of Connectivity for Sensor Networks Using Geometrical Probability." In Embedded and Ubiquitous Computing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30121-9_57.

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Remeijer, Peter, Coen Rasch, Joos V. Lebesque, and Marcel van Herk. "Defining margins for systematic rotations and translations: A probability based geometrical approach." In The Use of Computers in Radiation Therapy. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59758-9_206.

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Chaskalovic, Joel, and Franck Assous. "From a Geometrical Interpretation of Bramble-Hilbert Lemma to a Probability Distribution for Finite Element Accuracy." In Finite Difference Methods. Theory and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11539-5_1.

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Tuckwell, Henry C. "Geometric probability." In Elementary Applications of Probability Theory. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-3290-7_2.

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Tuckwell, Henry C. "Geometric probability." In Elementary Applications of Probability Theory. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1221-2_2.

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Kalashnikov, Vladimir. "Ruin Probability." In Geometric Sums: Bounds for Rare Events with Applications. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-1693-2_6.

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Borodin, Andrei N., and Paavo Salminen. "9. Geometric Brownian Motion." In Probability and Its Applications. Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8163-0_16.

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Nelson, Randolph. "Matrix Geometric Solutions." In Probability, Stochastic Processes, and Queueing Theory. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2426-4_9.

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Conference papers on the topic "Geometrical Probability"

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Appleby, D. M., Luigi Accardi, Guillaume Adenier, et al. "SIC-POVMS and MUBS: Geometrical Relationships in Prime Dimension." In FOUNDATIONS OF PROBABILITY AND PHYSICS—5. AIP, 2009. http://dx.doi.org/10.1063/1.3109944.

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Jaeger, Gregg. "Entanglement and Symmetry in Multiple-Qubit States: a geometrical approach." In FOUNDATIONS OF PROBABILITY AND PHYSICS - 3. AIP, 2005. http://dx.doi.org/10.1063/1.1874571.

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Ullah, Irshad, Mhd Nor Ramdon Bin Bahrom, Hussein Ahmed, Luqman Hakeem Bin Mahmood, and Zainab Zainal. "Lightning strike probability on macro geometrical structures." In 2016 IEEE International Conference on Power and Energy (PECon). IEEE, 2016. http://dx.doi.org/10.1109/pecon.2016.7951575.

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Bashllari, Alfred, Niko Kaciroti, Dritan Nace, and Akli Fundo. "Conflict Probability Estimations Based on Geometrical and Bayesian Approaches." In 2007 IEEE Intelligent Transportation Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/itsc.2007.4357787.

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Zhuang, Yanyan, and Jianping Pan. "A geometrical probability approach to location-critical network performance metrics." In IEEE INFOCOM 2012 - IEEE Conference on Computer Communications. IEEE, 2012. http://dx.doi.org/10.1109/infcom.2012.6195555.

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Aida, T. "Recognition and geometrical on-line learning algorithm of probability distributions." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.861300.

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Khalid, Z., and S. Durrani. "Connectivity of three dimensional wireless sensor networks using geometrical probability." In 2013 Australian Communications Theory Workshop (AusCTW 2013). IEEE, 2013. http://dx.doi.org/10.1109/ausctw.2013.6510043.

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Lichtenauer, Jeroen F., Iwan Setyawan, Ton Kalker, and Reginald L. Lagendijk. "Exhaustive geometrical search and the false positive watermark detection probability." In Electronic Imaging 2003, edited by Edward J. Delp III and Ping W. Wong. SPIE, 2003. http://dx.doi.org/10.1117/12.503186.

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Muchkaev, V. Y., and V. A. Tsarev. "Investigation of probability of optimisation of geometrical dimensions of double-gap cavities." In 2010 20th International Crimean Conference "Microwave & Telecommunication Technology" (CriMiCo 2010). IEEE, 2010. http://dx.doi.org/10.1109/crmico.2010.5632586.

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Ahmadi, Maryam, Minming Ni, and Jianping Pan. "A geometrical probability-based approach towards the analysis of uplink inter-cell interference." In 2013 IEEE Global Communications Conference (GLOBECOM 2013). IEEE, 2013. http://dx.doi.org/10.1109/glocomw.2013.6855735.

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Reports on the topic "Geometrical Probability"

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Boland, Philip J., Frank Proschan, and Y. L. Tong. Moment and Geometric Probability Inequalities Arising from Arrangement Increasing Functions. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada161273.

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Singer, D. A., and R. Kouda. Application of geometric probability and Bayesian statistics to the search for mineral deposits. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128119.

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Tarko, Andrew P., Qiming Guo, and Raul Pineda-Mendez. Using Emerging and Extraordinary Data Sources to Improve Traffic Safety. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317283.

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The current safety management program in Indiana uses a method based on aggregate crash data for conditions averaged over several-year periods with consideration of only major roadway features. This approach does not analyze the risk of crashes potentially affected by time-dependent conditions such as traffic control, operations, weather and their interaction with road geometry. With the rapid development of data collection techniques, time-dependent data have emerged, some of which have become available for safety management. This project investigated the feasibility of using emerging and exi
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