Prof. Lorí Viali |
Textos |
Apostilas | Eslaides | Exercícios | Laboratórios | Vídeos |
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Abaixo estão alguns textos relacionados com a disciplina. Eles podem ser utilizados para uma formação adicional. A maior parte está relacionada a aspectos históricos do MMC (Método Monte Carlo). |
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Anderson, Herbert Lawrence. Metropolis, Monte Carlo, and the MANIAC. Los Alamos Science, Fall, 1986, p. 96-108. ANTOCH, Jaromír. Environment for Statistical Computing. Computer Science Review, v. 2, 2008, p. 113-22. CASELLA, George, GEORGE, Edward I. Expalining the Gibbs Sampler. The American Statistician , v. 46, n. 3, August 1992, p. 167-74. CASELLA, George, ROBERT, Christian.A Short History of Makov Chain Monte Carlo - Subjetive Recollections from Incomplete Data. Statistical Science, v. 26, n. 1, February 2011, p. 102-15. DICICCIO, Thomas J., EFRON, Bradley. Bootstrap Confidence Intervals. Statistical Science, v. 11, n. 3, 1996, p. 189-228. Duckworth, William M., Stephenson, W. Robert. Resampling Methods: Not just for statisticians anymore. ECKHARDT, Roger. Stan Ulam, John Von Neumann, and the Monte Carlo Method. Los Alamos Science, Special Issue, 1987, p. 131-41. EFRON, Bradley. Second Thoughts on the Bootstrap. Statistical Science, v. 18, n. 2, 2003, p. 135-40. FERNANDES, Fernando M. S. Silva. Cinquentenário da Simulação Computacional em Mecânica Estatística. I. Os primeiros passos. Boletim da Sociedade Portuguesa de Química, v. 90, n. 39, 2003, p. 39-43. HARLOW, Francis Harvey, METROPOLIS, Nicholas. Computing & Computers: Weapons Simulation Leads to the Computer Era. Los Alamos Science, Winter/Spring, 1983. HURLEY, W. J. Resampling Calculations in a Spreasheet. Decision Line, September/October 2000. GENTLE, James E. Courses in Statistical Computing and Computational Statistics. The American Statistician, v. 58, n. 1, Feb 2004; p. 1-5. GENTLE, James E., Härdle, Wolfgang Karl, MORI, Yuichi. Computational Statistics: An Introduction. 16 p. JANSON, Birger. Generation of Random Bivariate Normal Deviates and Computation of Related Integrals. Bit, v. 4, 1964, p. 205-12. Jornada, Daniel Homrich da, Pizzolato, Morgana. Uso de Planilhas Eletrônicas para a Implementação do Método Monte Carlo para Estimativa da Incerteza de Medição. ENQUALAB-2005 - Encontro para a Qualidade de Laboratórios 7 a 9 de junho de 2005, São Paulo, Brasil. NATALE, Carlo Lauro. Computational Statistics or Statistical Computing, is that the question? The Statistical Software Newsletter. Editorial: Statement presented during COMPSTAT '96, p. 191-93. MARTINEZ, Edson Zangiacomi, LOUZADA-NETO, Francisco. Estimação Intervalar via Bootstrap. Rev. Mat. Estat., São Paulo, v. 19, 2001, p. 217-51. Metropolis, Nicholas. The Beginning of the Monte Carlo Method. Los Alamos Science, Special Issue, 1987, p. 125-30. Metropolis, Nicholas. ULAM, Stanislaw. The Monte Carlo Method. Journal of the American Statitiscal Association. v. 44, n. 247, Sep. 1949, p. 335-41. MULLER, Mervin E. A Comparison of Methods for Generating Normal Deviates on Digital Computers. Journal of the ACM (JACM), v. 6, n. 3, 1959, p. 376-83. MULLER, Mervin E., BOX, George E. P. A Note on the Generation of Random Normal Deviates. The Annals of Mathematical Statistics, v. 29, n. 2, 1958, p. 610-11. PRESS, Gil. A Very Short History of Data Science. Forbes, July 27, 2016, 9 p. RESNIK, Philip, HARDISTY, Eric. Gibbs Sampling for the uninitiated. June 2010. 22p. TUKEY, John Wilder. The Future of Data Analysis. The Annals of Mathematical Statistics, v. 33, n. 1, Mar. 1962, p. 1-67. ULAM, Stanislaw. On The Monte Carlo Method. WILKINSON, Leland. The Future of Statistical Computing. Technometrics, v. 50, n. 4, Nov., 2008, p. 418-35. YU, Chong Ho. Advance in Monte Carlo Simulations and Robustness Study and their Implications for the Dispute in Philosophy of Mathematics. Minerva - An Internet Journal of Philosophy. v. 8, 2004, p. 62-90. YU, Chong Ho. Resampling methods: Concepts, applications, and justification. Practical Assessment Research and Evaluation, v. 8, n. 19, p. 2003. |
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Material Didático |
Mat2274 |