Model-free objective Bayesian prediction.
J. M. BERNARDO (1999)
Revista de la Real Academia de Ciencias Exactas Físicas y Naturales
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J. M. BERNARDO (1999)
Revista de la Real Academia de Ciencias Exactas Físicas y Naturales
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David Ríos Insua, Raquel Montes Díez, Jesús Palomo Martínez (2002)
RACSAM
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Hydrology and water resources management are inherently affected by uncertainty in many of their involved processes, including inflows, rainfall, water demand, evaporation, etc. Statistics plays, therefore, an essential role in their study. We review here some recent advances within Bayesian statistics and decision analysis which will have a profound impact in these fields.
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).
Samir K. Bhattacharya, Ravinder K. Tyagi (1991)
Trabajos de Estadística
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This paper discusses the Bayesian reliability analysis for an exponential failure mode on the basis of some ordered observations when the first p observations may represent early failures or outliers. The Bayes estimators of the mean life and reliability are obtained for the underlying parametric model referred to as the SB(p) model under the assumption of the squared error loss function, the inverted gamma prior for scale parameter and a generalized uniform prior for the nuisance parameter. ...
Geliazkova, Maya (2010)
Serdica Journal of Computing
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We develop, implement and study a new Bayesian spatial mixture model (BSMM). The proposed BSMM allows for spatial structure in the binary activation indicators through a latent thresholded Gaussian Markov random field. We develop a Gibbs (MCMC) sampler to perform posterior inference on the model parameters, which then allows us to assess the posterior probabilities of activation for each voxel. One purpose of this article is to compare the HJ model and the BSMM in terms of receiver operating characteristics...
A. JUSTEL and D. PEÑA (1999)
Revista de la Real Academia de Ciencias Exactas Físicas y Naturales
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Arthur P. Dempster (1980)
Trabajos de Estadística e Investigación Operativa
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The task of assessing posterior distributions from noisy empirical data imposes difficult requirements of modelling, computing and assessing sensitivity to model choice. Seasonal analysis of economic time series is used to illustrate ways of approaching such difficulties.
A. Philip Dawid (1980)
Trabajos de Estadística e Investigación Operativa
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The elimination of nuisance parameters has classically been tackled by various ad hoc devices, and has led to a number of attemps to define partial sufficiency and ancillarity. The Bayesian approach is clearly defined. This paper examines some classical procedures in order to see when they can be given a Bayesian justification.
William F. Eddy, Anthony O'Hagan, José M. Bernardo, Philip J. Brown, A. Philip Dawid, James M. Dickey, Irving John Good, Adrian F. M. Smith (1980)
Trabajos de Estadística e Investigación Operativa
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Discussion on the papers by Freeman, Peter R., On the number of outliers in data from a linear model and by Box, George E. P., Sampling inference, Bayes' inference and robustness in the advancement of learning, both of them part of a round table on Sensitivity to models held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979).