منابع مشابه
Sequential Imputations and Bayesian Missing Data Problems
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Missing variable models are typical benchmarks for new computational techniques in that the ill-posed nature of missing variable models offer a challenging testing ground for these techniques. This was the case for the EM algorithm and the Gibbs sampler, and this is also true for importance sampling schemes. A population Monte Carlo scheme taking advantage of the latent structure of the problem...
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Figure skating is becoming increasingly popular as both a recreational and competitive sport. As the number of figure skating participants increases, so will the number of active patients who present to their primary care physician with sport-related injuries and medical problems. Figure skating is a unique sport that continues to evolve and progress with participants partaking in more difficul...
متن کاملMissing Data Problems in Machine Learning
Missing Data Problems in Machine Learning Benjamin M. Marlin Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. This thesis focuses on the problems of collaborative prediction with non-random missing data and classific...
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ژورنال
عنوان ژورنال: The Mathematical Gazette
سال: 1921
ISSN: 0025-5572
DOI: 10.2307/3604626