Bayesian density estimation.
Ghosh, Jayanta K. (1998)
Documenta Mathematica
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Ghosh, Jayanta K. (1998)
Documenta Mathematica
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Udi E. Makov (1980)
Trabajos de Estadística e Investigación Operativa
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Computational constraints often limit the practical applicability of coherent Bayes solutions to unsupervised sequential learning problems. These problems arise when attemps are made to learn about parameters on the basis of unclassified observations., each stemming from any one of k cases (k ≥ 2). In this paper, the difficulties of the Bayes process will be discussed and existing approximate learning procedures will be reviewed for broad types of problems involving mixtures...
Irving John Good (1980)
Trabajos de Estadística e Investigación Operativa
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A standard tecnique in subjective Bayesian methodology is for a subject (you) to make judgements of the probabilities that a physical probability lies in various intervals. In the Bayesian hierarchical technique you make probability judgements (of a higher type, order, level or stage) concerning the judgements of lower type. The paper will outline some of the history of this hierarchical technique with emphasis on the contributions by I. J. Good because I have read every word written...
Ghitany, M.E. (1990)
International Journal of Mathematics and Mathematical Sciences
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Irving John Good, Joseph B. Kadane, Tom Leonard, Anthony O'Hagan, Adrian F. M. Smith (1980)
Trabajos de Estadística e Investigación Operativa
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Discussion on the paper by Dalal, Sid R., Nonparametric Bayes decision theory, part of a round table on Bayesian non-parametric theory held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).
Irwin Guttman, S. James Press (1980)
Trabajos de Estadística e Investigación Operativa
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Discussion on the paper by Geisser, Seymour, Predictive sample reuse techniques for censored data, part of a round table on Bayesian and non-Bayesian conditional inference held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).
Hirotugu Akaike (1980)
Trabajos de Estadística e Investigación Operativa
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In this paper the likelihood function is considered to be the primary source of the objectivity of a Bayesian method. The necessity of using the expected behaviour of the likelihood function for the choice of the prior distribution is emphasized. Numerical examples, including seasonal adjustment of time series, are given to illustrate the practical utility of the common-sense approach to Bayesian statistics proposed in this paper.
Stephen E. Fienberg, José M. Bernardo, Philip J. Brown, A. Philip Dawid, James M. Dickey, Joseph B. Kadane, Tom Leonard (1980)
Trabajos de Estadística e Investigación Operativa
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Discussion on the papers by Makov, Udi E., Approximation of unsupervised Bayes learning procedures, Smith, Adrian F. M., Change-Point problems: approaches and applications and by Harrison, P. J. and Smith Jim Q., Discontinuity, decision and conflict, the three of them part of a round table on Sequential learning, discontinuities and changes held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).