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By David S. Moore (auth.), S. Panchapakesan, N. Balakrishnan (eds.)

Shanti S. Gupta has made pioneering contributions to rating and choice thought; particularly, to subset choice thought. His checklist of courses and the various citations his guides have bought during the last 40 years will amply testify to this truth. in addition to rating and choice, his pursuits contain order facts and reliability idea. the 1st editor's organization with Shanti Gupta is going again to 1965 while he got here to Purdue to do his Ph.D. He has the great fortune of being a pupil, a colleague and a long-standing collaborator of Shanti Gupta. the second one editor's organization with Shanti Gupta started in 1978 while he begun his study within the quarter of order facts. up to now two decades, he has collaborated with Shanti Gupta on numerous guides. We either suppose that our lives were enriched by way of our organization with him. He has certainly been a chum, thinker and advisor to us.

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152. Bayes and empirical Bayes rules for selecting multinomial populations (with T. , J. K. Ghosh, S. K. Mitra, K. R. Parthasarathy and B. L. S. Prakasa Rao) , pp. 265-278, Calcutta: Indian Statistical Institute. 153. Single-stage Bayes and empirical Bayes rules for ranking multinomial events (with S. N. Rande), Journal of Statistical Planning and Inference, 35, 367-382. 154. Selection and screening procedures in multivariate analysis (with S. , C. R. Rao), Chapter 12, pp. V .. 155. Selecting the best exponential population based on type-I censored data (with T.

They often confuse probability statements about the method (standard inference) and probability statements about the conclusion (Bayesian inference). " The Bayesian conclusion is easy to state, but hard to explain. What is this "probability 95%"? Physical and personal probabilities are conceptually quite different, and the user of Bayes methods must be aware that probabilities are conditioned on different events at each stage. That a user gives a semantically correct Bayesian conclusion is not evidence that she understands that conclusion.

That a user gives a semantically correct Bayesian conclusion is not evidence that she understands that conclusion. Bayes for Beginners? 8 15 What Spirit for Elementary Statistics Instruction? The continuing revolution in computing has changed the professional practice of statistics and our judgment of what constitutes interesting research in statistics. These changes are in turn changing the teaching of elementary statistics. We are moving from an over-emphasis on probability and formal inference toward a balanced presentation of data analysis, data production, and inference.

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