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Bayesians Can Learn from Old Data William H. Jefferys University of Texas at Austin, Department of Astronomy University of Vermont, Department of Mathematics and Statistics Email: May 14, 2007

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Probabilistic Deterministic Infinite Automata Nicholas Bartlett Frank Wood Department of Statistics Columbia University New York, NY 10027, USA

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Linear Submodular Bandits and their Application to Diversified Retrieval Carlos Guestrin Machine Learning Department Carnegie Mellon University

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Laws of Probability, Bayes’ theorem, and the Central Limit Theorem 2015 Penn State Astrostatistics Summer School David Hunter Department of Statistics Penn State University

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Risk Aggregation and Fr´echet Problems Part II - Complete and Joint Mixability Ruodu Wang http://sas.uwaterloo.ca/~wang Department of Statistics and Actuarial Science

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Counterfeiting and piracy 2014 Intellectual Property Law Statistics from the FCA Foto: stop-piracy.ch Federal Department of Finance FDF

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Confidence bands in nonparametric errors-in-variables regression Aurore Delaigle, Peter Hall and Farshid Jamshidi Department of Mathematics and Statistics, University of Melbourne, VIC, 3010, Australia Abstract: Errors

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Microsoft Word - SG Caribbean_World Statistics Day 2010 Message

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Brains, Meaning and Corpus Statistics Tom M. Mitchell and collaborators Machine Learning Department Carnegie Mellon University May, 2009

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