893 resultados para likelihood to publication


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With: Confession d'un biographe ... / par un ex-associé Pierre Mazerolle. Paris : Chez l'auteur, 1857. Bound together subsequent to publication.

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Iz knigi Mīrovai︠a︡ voĭna / K. Gelʹferikha -- Ėkonomicheskoe polozhenīe Germanīi -- Versalʹskīĭ mir i bolʹshevizm.

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With: Bortom godt och ondt /af Friedrich Nietzsche. Stockholm : Bonniers, [1904]. Bound together subsequent to publication.

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With: Dikt och verklighet / af Helena Nyblom. Stockholm : Norstedt, 1890. Bound together subsequent to publication.

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"Festgabe zur Feier der vierzigjährigen Anwesenheit Friedrich Creuzers."

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Special t.-p., v. 1-4: Geschichte der Philosophie alter Zeit; v. 5-12: Geschichte der christlichen Philosophie; added special t.-p., v. 9-12: Geschichte der neuern Philosophie.

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L111.A72 no. 376 with: Methods used by college social science departments to improve students' understanding of post-World War II international tensions / by Jennings B. Sanders. Washington, D.C. : Office of Education, 1952. Bound together subsequent to publication.

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With: Herrn Hermann Boerhaaves Beschreibung der Arzneymittel und Recepten. In Wien : Im Verlag der Kraussischen Buchhandlung, 1775. Bound together subsequent to publication.

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Bound together subsequent to publication.

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Followed by Kitāb al-manāẓir, by Euclides; al-Kurah al-mutaḥarrikah, by Autdycus; Maqāleh-yi panjum ... ; Kitāb al-masākin, by Theodosius; Kitāb al-Muʻṭayāt.

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Bound subsequent to publication.

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DU1 .I5 1936 vol. 13, no. 5 with : Business and government under the National Recovery Administration / Theodore J. Kreps. New York : American Council, Institute of Pacific Relations, 1936. Bound together subsequent to publication.

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In most treatments of the regression problem it is assumed that the distribution of target data can be described by a deterministic function of the inputs, together with additive Gaussian noise having constant variance. The use of maximum likelihood to train such models then corresponds to the minimization of a sum-of-squares error function. In many applications a more realistic model would allow the noise variance itself to depend on the input variables. However, the use of maximum likelihood to train such models would give highly biased results. In this paper we show how a Bayesian treatment can allow for an input-dependent variance while overcoming the bias of maximum likelihood.