Relevant bibliographies by topics / Statistical computation

Contents

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

Academic literature on the topic 'Statistical computation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Statistical computation"

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Kemp, A. W., and J. H. Maindonald. "Statistical Computation." Biometrics 42, no. 4 (1986): 1004. http://dx.doi.org/10.2307/2530723.

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Heller, Barbara. "Statistical computation." Mathematical Modelling 7, no. 9-12 (1986): 1658–59. http://dx.doi.org/10.1016/0270-0255(86)90103-x.

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Nelson, Lloyd S. "Statistical Computation." Journal of Quality Technology 18, no. 4 (1986): 259. http://dx.doi.org/10.1080/00224065.1986.11979022.

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Weisberg, Sanford, and J. H. Maindonald. "Statistical Computation." Journal of the American Statistical Association 80, no. 392 (1985): 1081. http://dx.doi.org/10.2307/2288606.

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Robert, Christian. "Statistical Modeling and Computation." CHANCE 27, no. 2 (2014): 61–62. http://dx.doi.org/10.1080/09332480.2014.914766.

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Hemmo, Meir, and Orly Shenker. "The Multiple-Computations Theorem and the Physics of Singling Out a Computation." Monist 105, no. 2 (2022): 175–93. http://dx.doi.org/10.1093/monist/onab030.

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Abstract The problem of multiple-computations discovered by Hilary Putnam presents a deep difficulty for functionalism (of all sorts, computational and causal). We describe in outline why Putnam’s result, and likewise the more restricted result we call the Multiple-Computations Theorem, are in fact theorems of statistical mechanics. We show why the mere interaction of a computing system with its environment cannot single out a computation as the preferred one amongst the many computations implemented by the system. We explain why nonreductive approaches to solving the multiple-computations pro
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Ordonez, Carlos. "Statistical Model Computation with UDFs." IEEE Transactions on Knowledge and Data Engineering 22, no. 12 (2010): 1752–65. http://dx.doi.org/10.1109/tkde.2010.44.

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Hertz, J. A. "Statistical Mechanics of Neural Computation." International Journal of Supercomputing Applications 2, no. 4 (1988): 54–62. http://dx.doi.org/10.1177/109434208800200406.

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Brooks, Stephen P. "Bayesian computation: a statistical revolution." Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 361, no. 1813 (2003): 2681–97. http://dx.doi.org/10.1098/rsta.2003.1263.

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Gentle, James E. "Statistical Computation (J. H. Maindonald)." SIAM Review 28, no. 2 (1986): 257–59. http://dx.doi.org/10.1137/1028077.

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Dissertations / Theses on the topic "Statistical computation"

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Liu, Meng-Chang. "Achieving privacy-preserving distributed statistical computation." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/achieving-privacypreserving-distributed-statistical-computation(6831db5c-d605-4a38-9711-7592d2b94e01).html.

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The growth of the Internet has opened up tremendous opportunities for cooperative computations where the results depend on the private data inputs of distributed participating parties. In most cases, such computations are performed by multiple mutually untrusting parties. This has led the research community into studying methods for performing computation across the Internet securely and efficiently. This thesis investigates security methods in the search for an optimum solution to privacy- preserving distributed statistical computation problems. For this purpose, the nonparametric sign test a
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Jiang, Huijing. "Statistical computation and inference for functional data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37087.

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My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part of my thesis focuses on developing modeling and inference procedure for functional data under spatial dependence. The methodology introduced in this part is motivated by a research study on inequities in accessibility to financial services. The first research problem in this part is concerned with a novel model-based method for clustering random time functions which are spatially interdependent. A cluster consists of time
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Ajayi, Ajibola. "Direct computation of statistical variations in electromagnetic problems." Thesis, University of Nottingham, 2008. http://eprints.nottingham.ac.uk/10539/.

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This work described in this thesis develops a computationally efficient approach to performing electromagnetic simulations in the presence of statistically defined uncertainties caused by either material inhomogeneities, or fabrication and placement tolerances. Comparisons are made with results from Monte Carlo simulations and a sequence of higher order approximation extensions is considered. There are two main techniques used to achieve the overall objective of this thesis namely: the Direct Solution Technique (DST) and the Unscented Transform (UT) method. The DST based on Taylor series appro
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Wong, Samuel Wing Kwong. "Statistical Computation for Problems in Dynamic Systems and Protein Folding." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11036.

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Inference for dynamic systems and conformational sampling for protein folding are two problems motivated by applied data, which pose computational challenges from a statistical perspective. Dynamic systems are often described by a set of coupled differential equations, and methods of parametric estimation for these models from noisy data can require repeatedly solving the equations numerically. Many of these models also lead to rough likelihood surfaces, which makes sampling difficult. We introduce a method for Bayesian inference on these models, using a multiple chain framework that exploi
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Qvarfordt, Viktor. "Non-Abelian Anyons: Statistical Repulsion and Topological Quantum Computation." Thesis, KTH, Matematik (Avd.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207177.

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As opposed to classical mechanics, quantum mechanical particles can be truly identical and lead to new and interesting phenomena. Identical particles can be of different types, determined by their exchange symmetry, which in turn gives rise to statistical repulsion. The exchange symmetry is given by a representation of the exchange group; the fundamental group of the configuration space of identical particles. In three dimensions the exchange group is the permutation group and there are only two types of identical particles; bosons and fermions. While any number of bosons can be at the same pl
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Simola, Umberto. "Developments in Approximate Bayesian Computation and Statistical Applications in Astrostatistics." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3423284.

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The title of this Thesis embraces two topics that have been investigated. Most of the present work is dedicated to develops and extensions for Approximate Bayesian Computation (ABC). While several algorithms have been proposed to improve the efficiency of the basic ABC algorithm, a number of subjective choices is left to the researcher. Several of these choices have not only an impact on the efficiency of the algorithm but also on its capability to approximate properly the true posterior distribution. We present a first extension of the ABC Population Monte-Carlo (ABC-PMC) algorithm aimed by t
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Adams, Niall. "Parallel processing for statistical computation with particular emphasis on bootstrap methods." Thesis, Liverpool John Moores University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388525.

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Jones, Lee K., and Richard C. 1943 Larson. "Efficient Computation of Probabilities of Events Described by Order Statistics and Application to a Problem of Queues." Massachusetts Institute of Technology, Operations Research Center, 1991. http://hdl.handle.net/1721.1/5159.

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Consider a set of N i.i.d. random variables in [0, 1]. When the experimental values of the random variables are arranged in ascending order from smallest to largest, one has the order statistics of the set of random variables. In this note an O(N3) algorithm is developed for computing the probability that the order statistics vector lies in a given rectangle. The new algorithm is then applied to a problem of statistical inference in queues. Illustrative computational results are included.
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Lieberman, Chad Eric. "Parameter and state model reduction for Bayesian statistical inverse problems." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54213.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 113-118).<br>Decisions based on single-point estimates of uncertain parameters neglect regions of significant probability. We consider a paradigm based on decision-making under uncertainty including three steps: identification of parametri
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Daly, Aidan C. "Statistical tools and community resources for developing trusted models in biology and chemistry." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:4e936e3b-7985-44f0-814c-7be3433bdcbb.

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Mathematical modeling has been instrumental to the development of natural sciences over the last half-century. Through iterated interactions between modeling and real-world exper- imentation, these models have furthered our understanding of the processes in biology and chemistry that they seek to represent. In certain application domains, such as the field of car- diac biology, communities of modelers with common interests have emerged, leading to the development of many models that attempt to explain the same or similar phenomena. As these communities have developed, however, reporting standa
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Books on the topic "Statistical computation"

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Kroese, Dirk P., and Joshua C.C. Chan. Statistical Modeling and Computation. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-8775-3.

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Chan, Joshua C. C., and Dirk P. Kroese. Statistical Modeling and Computation. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4132-3.

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George C. Marshall Space Flight Center., ed. Statistical computation of tolerance limits. National Aeronautics and Space Administration, George C. Marshall Space Flight Center, 1993.

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Krishnamoorthy, K. Statistical tolerance regions: Theory, applications, and computation. Wiley, 2009.

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Thisted, Ronald A. Elements of statistical computing: Numerical computation. Chapman and Hall, 1988.

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Fung, Wing Kam. Statistical DNA forensics: Theory, methods, and computation. John Wiley & Sons, 2008.

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R, Nelson Peter, ed. The Frontiers of statistical computation, simulation & modeling. American Sciences Press, 1990.

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Mezard, Marc. Information, physics, and computation. Oxford University Press, 2009.

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Mezard, Marc. Information, physics, and computation. Oxford University Press, 2009.

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Food and Agriculture Organization of the United Nations. Statistics Division., ed. Guidelines for the computation of selected statistical indicators. Food and Agriculture Organization of the United Nations, 1986.

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Book chapters on the topic "Statistical computation"

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Seber, George A. F., and Matthew R. Schofield. "Statistical Computation." In Statistics for Biology and Health. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18187-1_15.

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Perrett, Jamis J. "Statistical Computation Methods." In Statistics and Computing. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-5557-9_10.

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Baragona, Roberto, Francesco Battaglia, and Irene Poli. "Evolutionary Computation." In Evolutionary Statistical Procedures. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16218-3_2.

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Kim, Jae Kwang, and Jun Shao. "Computation." In Statistical Methods for Handling Incomplete Data, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429321740-3.

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Kroese, Dirk P., and Joshua C. C. Chan. "Statistical Inference." In Statistical Modeling and Computation. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8775-3_5.

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Titterington, D. Michael. "Statistical Modeling and Computation." In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives. John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470090456.ch17.

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Aliverti, Emanuele. "Bayesian Computation." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2025. https://doi.org/10.1007/978-3-662-69359-9_52.

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Kurosawa, Kaoru, and Osamu Watanabe. "Computational and statistical indistinguishabilities." In Algorithms and Computation. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56279-6_95.

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Kroese, Dirk P., and Joshua C. C. Chan. "Common Statistical Models." In Statistical Modeling and Computation. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8775-3_4.

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Rohde, Charles A. "Bayesian Statistics: Computation." In Introductory Statistical Inference with the Likelihood Function. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10461-4_15.

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Conference papers on the topic "Statistical computation"

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Brito, Nuno, Orlando Oliveira, and Paulo J. Silva. "High statistical computation of the Landau gauge ghost-gluon vertex." In The 41st International Symposium on Lattice Field Theory. Sissa Medialab, 2025. https://doi.org/10.22323/1.466.0469.

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Gibbs, Alison L., and Alex Stringer. "The Fundamental Role of Computation in Teaching Statistical Theory." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.rmcxl.

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What skills, knowledge and habits of mind does a statistician require in order to contribute effectively as an inhabitant of the data science ecosystem? We describe a new course in statistical theory that was developed as part of our consideration of this question. The course is a core requirement in a new curriculum for undergraduate students enrolled in statistics programs of study. Problem solving and critical thinking are developed through both mathematical and computational thinking and all ideas are motivated through questions to be answered from large, open and messy data. Central to th
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Mézard, Marc. "Statistical physics and statistical inference." In GECCO '21: Genetic and Evolutionary Computation Conference. ACM, 2021. http://dx.doi.org/10.1145/3449639.3465420.

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Wineberg, Mark, and Steffen Christensen. "Statistical analysis for evolutionary computation." In the 11th annual conference companion. ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570409.

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Wineberg, Mark, and Steffen Christensen. "Statistical analysis for evolutionary computation." In the 11th annual conference companion. ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570414.

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Wineberg, Mark. "Statistical analysis for evolutionary computation." In Proceeding of the fifteenth annual conference companion. ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2482678.

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Wineberg, Mark. "Statistical analysis for evolutionary computation." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2605341.

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Wineberg, Mark. "Statistical analysis for evolutionary computation." In the fourteenth international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330922.

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Wineberg, Mark, and Steffen Christensen. "Statistical analysis for evolutionary computation." In the 12th annual conference comp. ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830901.

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Wineberg, Mark, and Steffen Christensen. "Statistical analysis for evolutionary computation." In the 12th annual conference comp. ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830913.

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Reports on the topic "Statistical computation"

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Orban, Levente. Lecture Short Notes: Covariance, Standardization, and Correlation: A Statistical Overview. Orban Foundation, 2025. https://doi.org/10.69642/2421.

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This article provides an extensive overview of key statistical concepts related to covariance, standardization, and correlation. These principles are fundamental in psychology and behavioral sciences, particularly in data analysis. The document refines and formalizes lecture content, incorporating detailed mathematical formulations, conceptual explanations, real-world applications, and R scripts for manual computation.
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Pebay, Philippe Pierre. Formulas for robust, one-pass parallel computation of covariances and arbitrary-order statistical moments. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/1028931.

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Pebay, Philippe Pierre, Timothy Terriberry, Hemanth Kolla, and Janine Camille Bennett. Formulas for robust, parallel computation of arbitrary-order, arbitrary-variate, statistical moments with arbitrary weights and compounding. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1504207.

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Nobile, F., Q. Ayoul-Guilmard, S. Ganesh, et al. D6.5 Report on stochastic optimisation for wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.04.

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This report presents the latest methods of optimisation under uncertainties investigated in the ExaQUte project, and their applications to problems related to civil and wind engineering. The measure of risk throughout the report is the conditional value at risk. First, the reference method is presented: the derivation of sensitivities of the risk measure; their accurate computation; and lastly, a practical optimisation algorithm with adaptive statistical estimation. Second, this method is directly applied to a nonlinear relaxation oscillator (FitzHugh–Nagumo model) with numerical experiments t
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Alvargonzález, Piluca, Marina Asensio, Cristina Barceló, et al. The Spanish Survey of Household Finances (EFF): description and methods of the 2020 wave. Banco de España, 2024. http://dx.doi.org/10.53479/36089.

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The Spanish Survey of Household Finances 2020 (EFF2020) provides detailed information on the income, assets, debt and spending of Spanish households as at end-2020. Given the lack of alternative statistical sources that provide detailed household-level information on these variables for the same households, the concurrence of this edition of the survey with the COVID-19 pandemic makes it unique, allowing for all these variables to be analysed together in an exceptional setting. As in previous editions, this paper provides a detailed description of the most relevant methodological aspects of th
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Sacks, Jerome. Instrumentation for Computational Statistical Research. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada195887.

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Gentile, Ann C., Youssef M. Marzouk, James M. Brandt, and Philippe Pierre Pebay. Meaningful statistical analysis of large computational clusters. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/958384.

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Thompson, David C., Joseph Maurice Rojas, and Philippe Pierre Pebay. Computational algebraic geometry for statistical modeling FY09Q2 progress. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/984161.

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Glynn, Peter W., and Donald L. Iglehart. Computational and Statistical Issues in Discrete-Event Simulation. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada210743.

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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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