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- DOI: 10.18129/B9.bioc.statTarget Statistical Analysis of Molecular Profiles. Bioconductor version: Release (3.11) A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics.
(Quasi) Monte Carlo Framework in Python 3
Project description
Quasi-Monte Carlo (QMC) methods are used to approximate multivariate integrals. They have four main components: an integrand, a discrete distribution, summary output data, and stopping criterion. Information about the integrand is obtained as a sequence of values of the function sampled at the Foundations of Computational Mathematics, 16(6):1631-1696, 2016. (springer link, arxiv link)
[2] Fred J. Hickernell, Lan Jiang, Yuewei Liu, and Art B. Owen, 'Guaranteed conservative fixed width confidence intervals via Monte Carlo sampling,' Monte Carlo and Quasi-Monte Carlo Methods 2012 (J. Dick, F.Y. Kuo, G. W. Peters, and I. H. Sloan, eds.), pp. 105-128, Springer-Verlag, Berlin, 2014. DOI: 10.1007/978-3-642-41095-6_5
[3] Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from http://gailgithub.github.io/GAIL_Dev/.
[4] Sou-Cheng T. Choi, 'MINRES-QLP Pack and Reliable Reproducible Research via Supportable Scientific Software,' Journal of Open Research Software, Volume 2, Number 1, e22, pp. 1-7, 2014.
[5] Sou-Cheng T. Choi and Fred J. Hickernell, 'IIT MATH-573 Reliable Mathematical Software' [Course Slides], Illinois Institute of Technology, Chicago, IL, 2013. Available from http://gailgithub.github.io/GAIL_Dev/.
[6] Daniel S. Katz, Sou-Cheng T. Choi, Hilmar Lapp, Ketan Maheshwari, Frank Loffler, Matthew Turk, Marcus D. Hanwell, Nancy Wilkins-Diehr, James Hetherington, James Howison, Shel Swenson, Gabrielle D. Allen, Anne C. Elster, Bruce Berriman, Colin Venters, 'Summary of the First Workshop On Sustainable Software for Science: Practice and Experiences (WSSSPE1),' Journal of Open Research Software, Volume 2, Number 1, e6, pp. 1-21, 2014.
[7] Fang, K.-T., and Wang, Y. (1994). Number-theoretic Methods in Statistics. London, UK: CHAPMAN & HALL
[8] Lan Jiang, Guaranteed Adaptive Monte Carlo Methods for Estimating Means of Random Variables, PhD Thesis, Illinois Institute of Technology, 2016.
[9] Lluis Antoni Jimenez Rugama and Fred J. Hickernell, 'Adaptive multidimensional integration based on rank-1 lattices,' Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D. Nuyens, eds.), Springer Proceedings in Mathematics and Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1411.1966, pp. 407-422.
[10] Kai-Tai Fang and Yuan Wang, Number-theoretic Methods in Statistics, Chapman & Hall, London, 1994.
[11] Fred J. Hickernell and Lluis Antoni Jimenez Rugama, 'Reliable adaptive cubature using digital sequences,' Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D. Nuyens, eds.), Springer Proceedings in Mathematics and Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1410.8615 [math.NA], pp. 367-383.
[12] Marius Hofert and Christiane Lemieux (2019). qrng: (Randomized) Quasi-Random Number Generators. R package version 0.0-7. https://CRAN.R-project.org/package=qrng.
[13] Faure, Henri, and Christiane Lemieux. “Implementation of Irreducible Sobol’ Sequences in Prime Power Bases,” Mathematics and Computers in Simulation 161 (2019): 13–22.
[14] M. B. Giles. 'Multi-level Monte Carlo path simulation,' Operations Research, 56(3):607-617, 2008. http://people.maths.ox.ac.uk/~gilesm/files/OPRE_2008.pdf.
[15] M. B. Giles. 'Improved multilevel Monte Carlo convergence using the Milstein scheme,' 343-358, in Monte Carlo and Quasi-Monte Carlo Methods 2006, Springer, 2008. http://people.maths.ox.ac.uk/~gilesm/files/mcqmc06.pdf.
[16] M. B. Giles and B. J. Waterhouse. 'Multilevel quasi-Monte Carlo path simulation,' pp.165-181 in Advanced Financial Modelling, in Radon Series on Computational and Applied Mathematics, de Gruyter, 2009. http://people.maths.ox.ac.uk/~gilesm/files/radon.pdf.
[17] Owen, A. B. 'A randomized Halton algorithm in R,' 2017. arXiv:1706.02808 [stat.CO]
[18] B. D. Keister, Multidimensional Quadrature Algorithms, 'Computers in Physics', 10, pp. 119-122, 1996.
[19] L’Ecuyer, Pierre & Munger, David. (2015). LatticeBuilder: A General Software Tool for Constructing Rank-1 Lattice Rules. ACM Transactions on Mathematical Software. 42. 10.1145/2754929.
[20] Fischer, Gregory & Carmon, Ziv & Zauberman, Gal & L’Ecuyer, Pierre. (1999). Good Parameters and Implementations for Combined Multiple Recursive Random Number Generators. Operations Research. 47. 159-164. 10.1287/opre.47.1.159.
[21] I.M. Sobol', V.I. Turchaninov, Yu.L. Levitan, B.V. Shukhman: 'Quasi-Random Sequence Generators' Keldysh Institute of Applied Mathematics, Russian Acamdey of Sciences, Moscow (1992).
[22] Sobol, Ilya & Asotsky, Danil & Kreinin, Alexander & Kucherenko, Sergei. (2011). Construction and Comparison of High-Dimensional Sobol' Generators. Wilmott. 2011. 10.1002/wilm.10056.
[23] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., … Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d extquotesingle Alch'e-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (pp. 8024–8035). Curran Associates, Inc. Retrieved from http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
[24] S. Joe and F. Y. Kuo, Constructing Sobol sequences with better two-dimensional projections, SIAM J. Sci. Comput. 30, 2635-2654 (2008).
[25] [1] Paul Bratley and Bennett L. Fox. 1988. Algorithm 659: Implementing Sobol's quasirandom sequence generator. ACM Trans. Math. Softw. 14, 1 (March 1988), 88–100. DOI:https://doi.org/10.1145/42288.214372
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Illinois Tech
Kamakura Corporation
SigOpt, Inc.
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