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Semi-parametric analysis of failure time data from case-control family studies on candidate genes /Chen, Lu, January 2004 (has links)
Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 97-102).
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Marginal modeling of longitudinal, binary response data : semiparametric and parametric estimation with long response series and an efficient outcome dependent sampling design /Schildcrout, Jonathan Scott, January 2004 (has links)
Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 139-144).
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Sieve analysis : statistical methods for assessing differential vaccine protection against human immunodeficiency virus types /Gilbert, Peter Brian. January 1996 (has links)
Thesis (Ph. D.)--University of Washington, 1996. / Includes bibliographical references (leaves [213]-237).
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Direkte EM- und Loglikelihood-Profil-Verfahren in Intervallkartierungsmodellen der PflanzenzuchtEmrich, Katharina. Unknown Date (has links) (PDF)
Universiẗat, Diss., 2002--Dortmund.
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Statistische Auswahl von Wortabhängigkeiten in der automatischen SpracherkennungMartin, Sven Carl. Unknown Date (has links) (PDF)
Techn. Hochsch., Diss., 2000--Aachen.
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Empirische Risiko-Minimierung bei zensierten DatenDöhler, Sebastian. Unknown Date (has links) (PDF)
Universiẗat, Diss., 2000--Freiburg (Breisgau).
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Maximum a posteriori models for cortical modeling feature detectors, topography and modularity /Weber, Cornelius. Unknown Date (has links) (PDF)
Techn. University, Diss., 2001--Berlin.
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Quantitative Analyse dynamischer nichtlinearer PanelmodelleBode, Oliver. Unknown Date (has links) (PDF)
Universiẗat, Diss., 2001--Göttingen.
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Beitrag zur skalenabhängigen Erfassung teilschlagspezifischer Pflanzenschäden mit Methoden der Fernerkundung und GeoinformationVoß, Kerstin. Unknown Date (has links) (PDF)
Universiẗat, Diss., 2005--Bonn.
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Methods and Experiments With Bounded Tree-width Markov NetworksLiang, Percy, Srebro, Nathan 30 December 2004 (has links)
Markov trees generalize naturally to bounded tree-width Markov networks, onwhich exact computations can still be done efficiently. However, learning themaximum likelihood Markov network with tree-width greater than 1 is NP-hard, sowe discuss a few algorithms for approximating the optimal Markov network. Wepresent a set of methods for training a density estimator. Each method isspecified by three arguments: tree-width, model scoring metric (maximumlikelihood or minimum description length), and model representation (using onejoint distribution or several class-conditional distributions). On thesemethods, we give empirical results on density estimation and classificationtasks and explore the implications of these arguments.
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