Academic literature on the topic 'Statistical graphs'
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Journal articles on the topic "Statistical graphs"
Haigh, William E. "Statistical Graphs and Logo." School Science and Mathematics 89, no. 3 (March 1989): 228–38. http://dx.doi.org/10.1111/j.1949-8594.1989.tb11914.x.
Full textNowicki, Krzysztof. "Asymptotic Poisson distributions with applications to statistical analysis of graphs." Advances in Applied Probability 20, no. 02 (June 1988): 315–30. http://dx.doi.org/10.1017/s0001867800016992.
Full textNowicki, Krzysztof. "Asymptotic Poisson distributions with applications to statistical analysis of graphs." Advances in Applied Probability 20, no. 2 (June 1988): 315–30. http://dx.doi.org/10.2307/1427392.
Full textGhafouri, Saeid, and Seyed Hossein Khasteh. "A survey on exponential random graph models: an application perspective." PeerJ Computer Science 6 (April 6, 2020): e269. http://dx.doi.org/10.7717/peerj-cs.269.
Full textBurda, Zdzisław, Jerzy Jurkiewicz, and André Krzywicki. "Statistical mechanics of random graphs." Physica A: Statistical Mechanics and its Applications 344, no. 1-2 (December 2004): 56–61. http://dx.doi.org/10.1016/j.physa.2004.06.087.
Full textLEWANDOWSKY, STEPHAN, and IAN SPENCE. "The Perception of Statistical Graphs." Sociological Methods & Research 18, no. 2-3 (November 1989): 200–242. http://dx.doi.org/10.1177/0049124189018002002.
Full textHosamani, S. M., V. B. Awati, and R. M. Honmore. "On graphs with equal dominating and c-dominating energy." Applied Mathematics and Nonlinear Sciences 4, no. 2 (December 24, 2019): 503–12. http://dx.doi.org/10.2478/amns.2019.2.00047.
Full textLivingston, Mark A., Laura Matzen, Derek Brock, Andre Harrison, and Jonathan W. Decker. "Testing the Value of Salience in Statistical Graphs." Electronic Imaging 2021, no. 1 (January 18, 2021): 329–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.1.vda-329.
Full textSalti, Dror, and Yakir Berchenko. "Random Intersection Graphs and Missing Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5579–85. http://dx.doi.org/10.1609/aaai.v34i04.6010.
Full textSudev, N. K., K. P. Chithra, K. A. Germina, S. Satheesh, and Johan Kok. "On certain coloring parameters of Mycielski graphs of some graphs." Discrete Mathematics, Algorithms and Applications 10, no. 03 (June 2018): 1850030. http://dx.doi.org/10.1142/s1793830918500301.
Full textDissertations / Theses on the topic "Statistical graphs"
Ruan, Da. "Statistical methods for comparing labelled graphs." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24963.
Full textCerqueira, Andressa. "Statistical inference on random graphs and networks." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-04042018-094802/.
Full textNessa tese estudamos dois modelos probabilísticos definidos em grafos: o modelo estocástico por blocos e o modelo de grafos exponenciais. Dessa forma, essa tese está dividida em duas partes. Na primeira parte nós propomos um estimador penalizado baseado na mistura de Krichevsky-Trofimov para o número de comunidades do modelo estocástico por blocos e provamos sua convergência quase certa sem considerar um limitante conhecido para o número de comunidades. Na segunda parte dessa tese nós abordamos o problema de simulação perfeita para o modelo de grafos aleatórios Exponenciais. Nós propomos um algoritmo de simulação perfeita baseado no algoritmo Coupling From the Past usando a dinâmica de Glauber. Esse algoritmo é eficiente apenas no caso em que o modelo é monotóno e nós provamos que esse é o caso para um subconjunto do espaço paramétrico. Nós também propomos um algoritmo de simulação perfeita baseado no algoritmo Backward and Forward que pode ser aplicado à modelos monótonos e não monótonos. Nós provamos a existência de um limitante superior para o número esperado de passos de ambos os algoritmos.
Vohra, Neeru Rani. "Three dimensional statistical graphs, visual cues and clustering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ56213.pdf.
Full textChandrasekaran, Venkat. "Convex optimization methods for graphs and statistical modeling." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66002.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 209-220).
An outstanding challenge in many problems throughout science and engineering is to succinctly characterize the relationships among a large number of interacting entities. Models based on graphs form one major thrust in this thesis, as graphs often provide a concise representation of the interactions among a large set of variables. A second major emphasis of this thesis are classes of structured models that satisfy certain algebraic constraints. The common theme underlying these approaches is the development of computational methods based on convex optimization, which are in turn useful in a broad array of problems in signal processing and machine learning. The specific contributions are as follows: -- We propose a convex optimization method for decomposing the sum of a sparse matrix and a low-rank matrix into the individual components. Based on new rank-sparsity uncertainty principles, we give conditions under which the convex program exactly recovers the underlying components. -- Building on the previous point, we describe a convex optimization approach to latent variable Gaussian graphical model selection. We provide theoretical guarantees of the statistical consistency of this convex program in the high-dimensional scaling regime in which the number of latent/observed variables grows with the number of samples of the observed variables. The algebraic varieties of sparse and low-rank matrices play a prominent role in this analysis. -- We present a general convex optimization formulation for linear inverse problems, in which we have limited measurements in the form of linear functionals of a signal or model of interest. When these underlying models have algebraic structure, the resulting convex programs can be solved exactly or approximately via semidefinite programming. We provide sharp estimates (based on computing certain Gaussian statistics related to the underlying model geometry) of the number of generic linear measurements required for exact and robust recovery in a variety of settings. -- We present convex graph invariants, which are invariants of a graph that are convex functions of the underlying adjacency matrix. Graph invariants characterize structural properties of a graph that do not depend on the labeling of the nodes; convex graph invariants constitute an important subclass, and they provide a systematic and unified computational framework based on convex optimization for solving a number of interesting graph problems. We emphasize a unified view of the underlying convex geometry common to these different frameworks. We describe applications of both these methods to problems in financial modeling and network analysis, and conclude with a discussion of directions for future research.
by Venkat Chandrasekaran.
Ph.D.
Piotet, Fabien. "Statistical properties of the eigenfunctions on quantum graphs." Thesis, University of Bristol, 2009. http://hdl.handle.net/1983/af3ec57a-5d16-4995-936f-c310696f1093.
Full textPhadnis, Miti. "Statistical Analysis of Linear Analog Circuits Using Gaussian Message Passing in Factor Graphs." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/504.
Full textHamdi, Maziyar. "Statistical signal processing on dynamic graphs with applications in social networks." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56256.
Full textApplied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
Alberici, Diego. "Statistical Mechanics of Monomer-Dimer Models on Complete and Erdös-Rényi Graphs." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4169/.
Full textDíaz-Levicoy, Danilo, Miluska Osorio, Pedro Arteaga, and Francisco Rodríguez-Alveal. "Gráficos estadísticos en libros de texto de matemática de educación primaria en Perú." BOLEMA Departamento de Matematica, 2018. http://hdl.handle.net/10757/624628.
Full textRevisión por pares
Leger, Jean-Benoist. "Modelling the topology of ecological bipartite networks with statistical models for heterogeneous random graphs." Paris 7, 2014. http://www.theses.fr/2014PA077185.
Full textAn ecological network is a representation of the whole set of interactions between species in a given context. Ecological scientists analyse the topological structure of such networks, in order to understand the underlying processes. The identification of sub-groups of highly-interacting species (usually called communities, or compartments) is an important stream of research. The most popular method for the search of communities in ecological networks is the modularity optimization method. However this popularity is more due to the first paper published on this topic than to a rational choice based on solid grounds. There are many other clustering methods that could be used to delimit communities in ecological networks. The analysis of complex networks is indeed a rapidly growing topic with many applications in several scientific fields. To our knowledge, no comparison of different clustering methods is available in the case of ecological networks. Here we reviewed the whole set of methods available for clustering networks and we compared them using an ecological benchmark. In order to assess the relative contribution of several processes to the network structure, we integrated exogenous information in the clustering model. We analysed two bipartite antagonistic networks with this method, a tree-fungus and tree-insect network. The results are still preliminary but the method seems to us very promising for future ecological studies. Finally we searched communities in a different kind of network, a mating network between individuals belonging to two hybridizing tree species. We used our results to discuss a concept which is central in ecology, the species concept
Books on the topic "Statistical graphs"
Random graphs for statistical pattern recognition. Hoboken, NJ: Wiley & Sons, 2002.
Find full textMarchette, David J. Random Graphs for Statistical Pattern Recognition. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/047172209x.
Full textJaroslav, Nešetřil, and Winkler P. 1946-, eds. Graphs, morphisms, and statistical physics: DIMACS Workshop Graphs, Morphisms and Statistical Physics, March 19-21, 2001, DIMACS Center. Providence, RI: American Mathematical Society, 2004.
Find full textAldrich, James O. Building SPSS graphs to understand data. Thousand Oaks: SAGE Publications, 2013.
Find full textAldrich, James O. Building SPSS graphs to understand data. Thousand Oaks: SAGE Publications, 2013.
Find full textW, Dyal William, Eddins Donald L, and Centers for Disease Control (U.S.), eds. Descriptive statistics: Tables, graphs, & charts. Atlanta, Ga: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, 1988.
Find full textVladas, Sidoravicius, and Smirnov S. (Stanislav) 1970-, eds. Probability and statistical physics in St. Petersburg: St. Petersburg School in Probability and Statistical Physics : June 18-29, 2012 : St. Petersburg State University, St. Petersburg, Russia. Providence, Rhode Island: American Mathematical Society, 2015.
Find full textInstitute, SAS, ed. SAS system for statistical graphics. Cary, NC: SAS Institute, 1991.
Find full textBook chapters on the topic "Statistical graphs"
Heiberger, Richard M., and Burt Holland. "Graphs." In Statistical Analysis and Data Display, 85–121. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_4.
Full textHeiberger, Richard M., and Burt Holland. "Graphs." In Statistical Analysis and Data Display, 63–89. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_4.
Full textHanna, Akram R. G., Christopher Rao, and Thanos Athanasiou. "Graphs in Statistical Analysis." In Key Topics in Surgical Research and Methodology, 441–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-71915-1_35.
Full textKolaczyk, Eric D., and Gábor Csárdi. "Statistical Models for Network Graphs." In Use R!, 85–109. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0983-4_6.
Full textBoutillier, Cédric, and Béatrice de Tilière. "Statistical Mechanics on Isoradial Graphs." In Probability in Complex Physical Systems, 491–512. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23811-6_20.
Full textKolaczyk, Eric D., and Gábor Csárdi. "Statistical Models for Network Graphs." In Use R!, 87–113. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44129-6_6.
Full textLoebl, Martin. "Geometric representations of graphs." In Discrete Mathematics in Statistical Physics, 77–100. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9329-1_5.
Full textKarwa, Vishesh, Aleksandra B. Slavković, and Pavel Krivitsky. "Differentially Private Exponential Random Graphs." In Privacy in Statistical Databases, 143–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11257-2_12.
Full textAndersson, Håkan, and Tom Britton. "Epidemics and graphs." In Stochastic Epidemic Models and Their Statistical Analysis, 63–72. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1158-7_7.
Full textMansmann, Ulrich, Markus Schmidberger, Ralf Strobl, and Vindi Jurinovic. "Indirect Comparison of Interaction Graphs." In Statistical Modelling and Regression Structures, 249–65. Heidelberg: Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2413-1_14.
Full textConference papers on the topic "Statistical graphs"
Karaaslanli, Abdullah, and Selin Aviyente. "Graph Learning From Noisy and Incomplete Signals on Graphs." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513838.
Full textBolanos, Marcos E., Selin Aviyente, and Hayder Radha. "Graph entropy rate minimization and the compressibility of undirected binary graphs." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319634.
Full textFerres, Leo, Petro Verkhogliad, Gitte Lindgaard, Louis Boucher, Antoine Chretien, and Martin Lachance. "Improving accessibility to statistical graphs." In the 9th international ACM SIGACCESS conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1296843.1296857.
Full textArteaga Cezón, Pedro, Jose Manuel Vigo, and Carmen Batanero. "READING LEVELS OF STATISTICAL GRAPHS." In 10th annual International Conference of Education, Research and Innovation. IATED, 2017. http://dx.doi.org/10.21125/iceri.2017.0719.
Full textAghagolzadeh, Mohammad, Iman Barjasteh, and Hayder Radha. "Transitivity matrix of social network graphs." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319644.
Full textHafidi, Hakim, Mounir Ghogho, Philippe Ciblat, and Ananthram Swami. "Bayesian Node Classification for Noisy Graphs." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513801.
Full textZens, Richard, and Hermann Ney. "Word graphs for statistical machine translation." In the ACL Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1654449.1654491.
Full textRao, Vasant, Debjit Sinha, Nitin Srimal, and Prabhat K. Maurya. "Statistical path tracing in timing graphs." In DAC '16: The 53rd Annual Design Automation Conference 2016. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2897937.2898096.
Full textTsitsvero, Mikhail, Pierre Borgnat, and Paulo Goncalves. "Multidimensional Analytic Signal with Application on Graphs." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450803.
Full textRomero, Daniel, Meng Ma, and Georgios B. Giannakis. "Estimating signals over graphs via multi-kernel learning." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551714.
Full textReports on the topic "Statistical graphs"
Miller, Willard, and Jr. Robustness, Diagnostics, Computing and Graphics in Statistics. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada222888.
Full textW. Davis and D. Mastrovito. DbAccess: Interactive Statistics and Graphics for Plasma Physics Databases. Office of Scientific and Technical Information (OSTI), October 2003. http://dx.doi.org/10.2172/820083.
Full textNewton, H. J. Computing Science and Statistics. Volume 24. Graphics and Visualization. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada265181.
Full textKennedy, C. R. Statistical characterization of three grades of large billet-graphites: Stackpole 2020, Union Carbide TS1792, and Toyo Tanso IG11. Office of Scientific and Technical Information (OSTI), September 1987. http://dx.doi.org/10.2172/770926.
Full textSchulz, Jan, Daniel Mayerhoffer, and Anna Gebhard. A Network-Based Explanation of Perceived Inequality. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49393.
Full textBrown, Yolanda, Twonia Goyer, and Maragaret Harvey. Heart Failure 30-Day Readmission Frequency, Rates, and HF Classification. University of Tennessee Health Science Center, December 2020. http://dx.doi.org/10.21007/con.dnp.2020.0002.
Full textLavadenz, Magaly, Sheila Cassidy, Elvira G. Armas, Rachel Salivar, Grecya V. Lopez, and Amanda A. Ross. Sobrato Early Academic Language (SEAL) Model: Final Report of Findings from a Four-Year Study. Center for Equity for English Learners, Loyola Marymount University, 2020. http://dx.doi.org/10.15365/ceel.seal2020.
Full textEIA publications manual: Statistical graphs. Office of Scientific and Technical Information (OSTI), April 1985. http://dx.doi.org/10.2172/6118047.
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