Computational Statistics in Data Science. Группа авторов

Читать онлайн.
Название Computational Statistics in Data Science
Автор произведения Группа авторов
Жанр Математика
Серия
Издательство Математика
Год выпуска 0
isbn 9781119561088



Скачать книгу

and Hobert, J.P. (2004) Sufficient burn‐in for Gibbs samplers for a hierarchical random effects model. Ann. Stat., 32, 784–817.

      37 37 Gupta, K. and Vats, D. (2020) Estimating Monte Carlo variance from multiple Markov chains. arXiv preprint arXiv:2007.04229.

      38 38 Dawkins, B. (1991) Siobhan's problem: the coupon collector revisited. Am. Stat., 45 (1), 76–82.

      39 39 Marske, D.M. (1967) BOD Data Interpretation Using the Sum of Squares Surface, University of Wisconsin, Madison.

      40 40 Bates, D.M. and Watts, D.G. (1988) Nonlinear Regression Analysis and Its Applications, vol. 2, Wiley, New York.

      41 41 Newton, M.A. and Raftery, A.E. (1994) Approximate Bayesian inference with the weighted likelihood bootstrap. J. R. Stat. Soc., Ser. B, 56, 3–26.

      42 42 Archila, F.H.A. (2016) Markov chain Monte Carlo for linear mixed models. PhD thesis. University of Minnesota.

      Конец ознакомительного фрагмента.

      Текст предоставлен ООО «ЛитРес».

      Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.

      Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.

/9j/4AAQSkZJRgABAQEBLAEsAAD/7SHoUGhvdG9zaG9wIDMuMAA4QklNBAQAAAAAAA8cAVoAAxsl RxwCAAACAAAAOEJJTQQlAAAAAAAQzc/6fajHvgkFcHaurwXDTjhCSU0EOgAAAAAA5QAAABAAAAAB AAAAAAALcHJpbnRPdXRwdXQAAAAFAAAAAFBzdFNib29sAQAAAABJbnRlZW51bQAAAABJbnRlAAAA AENscm0AAAAPcHJpbnRTaXh0ZWVuQml0Ym9vbAAAAAALcHJpbnRlck5hbWVURVhUAAAAAQAAAAAA D3ByaW50UHJvb2ZTZXR1cE9iamMAAAAMAFAAcgBvAG8AZgAgAFMAZQB0AHUAcAAAAAAACnByb29m U2V0dXAAAAABAAAAAEJsdG5lbnVtAAAADGJ1aWx0aW5Qcm9vZgAAAAlwcm9vZkNNWUsAOEJJTQQ7 AAAAAAItAAAAEAAAAAEAAAAAABJwcmludE91dHB1dE9wdGlvbnMAAAAXAAAAAENwdG5ib29sAAAA AABDbGJyYm9vbAAAAAAAUmdzTWJvb2wAAAAAAENybkNib29sAAAAAABDbnRDYm9vbAAAAAAATGJs c2Jvb2wAAAAAAE5ndHZib29sAAAAAABFbWxEYm9vbAAAAAAASW50cmJvb2wAAAAAAEJja2dPYmpj AAAAAQAAAAAAAFJHQkMAAAADAAAAAFJkICBkb3ViQG/gAAAAAAAAAAAAR3JuIGRvdWJAb+AAAAAA AAAAAABCbCAgZG91YkBv4AAAAAAAAAAAAEJyZFRVbnRGI1JsdAAAAAAAAAAAAAAAAEJsZCBVbnRG I1JsdAAAAAAAAAAAAAAAAFJzbHRVbnRGI1B4bEBywAAAAAAAAAAACnZlY3RvckRhdGFib29sAQAA AABQZ1BzZW51bQAAAABQZ1BzAAAAAFBnUEMAAAAATGVmdFVudEYjUmx0AAAAAAAAAAAAAAAAVG9w IFVudEYjUmx0AAAAAAAAAAAAAAAAU2NsIFVudEYjUHJjQFkAAAAAAAAAAAAQY3JvcFdoZW5Qcmlu dGluZ2Jvb2wAAAAADmNyb3BSZWN0Qm90dG9tbG9uZwAAAAAAAAAMY3JvcFJlY3RMZWZ0bG9uZwAA AAAAAAANY3JvcFJlY3RSaWdodGxvbmcAAAAAAAAAC2Nyb3BSZWN0VG9wbG9uZwAAAAAAOEJJTQPt AAAAAAAQASwAAAABAAIBLAAAAAEAAjhCSU0EJgAAAAAADgAAAAAAAAAAAAA/gAAAOEJJTQQNAAAA AAAEAAAAWjhCSU0EGQAAAAAABAAAAB44QklNA/MAAAAAAAkAAAAAAAAAAAEAOEJJTScQAAAAAAAK AAEAAAAAAAAAAjhCSU0D9QAAAAAASAAvZmYAAQBsZmYABgAAAAAAAQAvZmYAAQChmZoABgAAAAAA AQAyAAAAAQBaAAAABgAAAAAAAQA1AAAAAQAtAAAABgAAAAAAAThCSU0D+AAAAAAAcAAA//////// /////////////////////wPoAAAAAP////////////////////////////8D6AAAAAD///////// ////////////////////A+gAAAAA/////////////////////////////wPoAAA4QklNBAAAAAAA AAIAAThCSU0EAgAAAAAABAAAAAA4QklNBDAAAAAAAAIBAThCSU0ELQAAAAAABgABAAAABDhCSU0E CAAAAAAAEAAAAAEAAAJAAAACQAAAAAA4QklNBB4AAAAAAAQAAAAAOEJJTQQaAAAAAANPAAAABgAA AAAAAAAAAAALQgAAB8AAAAANADkANwA4ADEAMQAxADkANQA2ADEAMAA3ADEAAAABAAAAAAAAAAAA AAAAAAAAAAAAAAEAAAAAAAAAAAAAB8AAAAtCAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAA AAAAAAAAEAAAAAEAAAAAAABudWxsAAAAAgAAAAZib3VuZHNPYmpjAAAAAQAAAAAAAFJjdDEAAAAE AAAAAFRvcCBsb25nAAAAAAAAAABMZWZ0bG9uZwAAAAAAAAAAQnRvbWxvbmcAAAtCAAAAAFJnaHRs b25nAAAHwAAAAAZzbGljZXNWbExzAAAAAU9iamMAAAABAAAAAAAFc2xpY2UAAAASAAAAB3NsaWNl SURsb25nAAAAAAAAAAdncm91cElEbG9uZwAAAAAAAAAGb3JpZ2luZW51bQAAAAxFU2xpY2VPcmln aW4AAAANYXV0b0dlbmVyYXRlZAAAAABUeXBlZW51bQAAAApFU2xpY2VUeXBlAAAAAEltZyAAAAAG Ym91bmRzT2JqYwAAAAEAAAAAAABSY3QxAAAABAAAAABUb3AgbG9uZwAAAAAAAAAATGVmdGxvbmcA AAAAAAAAAEJ0b21sb25nAAALQgAAAABSZ2h0bG9uZwAAB8AAAAADdXJsVEVYVAAAAAEAAAAAAABu dWxsVEVYVAAAAAEAAAAAAABNc2dlVEVYVAAAAAEAAAAAAAZhbHRUYWdURVhUAAAAAQAAAAAADmNl bGxUZXh0SXNIVE1MYm9vbAEAAAAIY2VsbFRleHRURVhUAAAAAQAAAAAACWhvcnpBbGlnbmVudW0A AAAPRVNsaWNlSG9yekFsaWduAAAAB2RlZmF1bHQAAAAJdmVydEFsaWduZW51bQAAAA9FU2xpY2VW ZXJ0QWxpZ24AAAAHZGVmYXVsdAAAAAtiZ0NvbG9yVHlwZWVudW0AAAARRVNsaWNlQkdDb2xvclR5 cGUAAAAATm9uZQAAAAl0b3BPdXRzZXRsb25nAAAAAAAAAApsZWZ0T3V0c2V0bG9uZwAAAAAAAAAM Ym90dG9tT3V0c2V0bG9uZwAAAAAAAAALcmlnaHRPdXRzZXRsb25nAAAAAAA4QklNBCgAAAAAAAwA AAACP/AAAAAAAAA4QklNBBQAAAAAAAQAAAAEOEJJTQQMAAAAABiwAAAAAQAAAG4AAACgAAABTAAA z4AAABiUABgAAf/Y/+0ADEFkb2JlX0NNAAH/7gAOQWRvYmUAZIAAAAAB/9sAhAAMCAgICQgMCQkM EQsKCxEVDwwMDxUYExMVExMYEQwMDAwMDBEMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMAQ0L Cw0ODRAODhAUDg4OFBQODg4OFBEMDAwMDBERDAwMDAwMEQwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAz/wAARCACgAG4DASIAAhEBAxEB/90ABAAH/8QBPwAAAQUBAQEBAQEAAAAAAAAAAwABAgQF BgcICQoLAQABBQEBAQEBAQAAAAAAAAABAAIDBAUGBwgJCgsQAAEEAQMCBAIFBwYIBQMMMwEAAhED BCESMQVBUWETInGBMgYUkaGxQiMkFVLBYjM0coLRQwclklPw4fFjczUWorKDJkSTVGRFwqN0NhfS VeJl8rOEw9N14/NGJ5SkhbSVxNTk9KW1xdXl9VZmdoaWprbG1ub2N0dXZ3eHl6e3x9fn9xEAAgIB AgQEAwQFBgcHBgU1AQACEQMhMRIEQVFhcSITBTKBkRShsUIjwVLR8DMkYuFygpJDUxVjczTxJQYW orKDByY1wtJEk1SjF2RFVTZ0ZeLys4TD03Xj80aUpIW0lcTU5PSltcXV5fVWZnaGlqa2xtbm9ic3 R1dnd4eXp7fH/9oADAMBAAIRAxEAPwDzBzn73e48nufFNvf+8fvKT/pu+J/KmSQvvf8AvH7ylvf+ 8fvKZJFS+9/7x+8pb3/vH7ymSSUvvf8AvH7ylvf+8fvKZJJS+9/7x+8pb3/vH7ymSSUvvf8AvH7y lvf+8fvKZJJS+9/7x+8p2ufDvcePE+L