Displaying similar documents to “Model-free objective Bayesian prediction.”

A Bayesian approach to cluster analysis.

José M. Bernardo, F.Javier Girón (1988)

Qüestiió

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A general probabilistic model for describing the structure of statistical problems known under the generic name of cluster analysis, based on finite mixtures of distributions, is proposed. We analyse the theoretical and practical implications of this approach, and point out some open question on both the theoretical problem of determining the reference prior for models based on mixtures, and the practical problem of approximation that mixtures typically entail. Finally, models based...

Bayesian reliability analysis when multiple early failures may be present.

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. ...

Bayesian methods in hydrology: a review.

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.

Bayesian survival analysis based on the Rayleigh model.

Samir K. Bhattacharya, K. Tyagi Ravinder (1990)

Trabajos de Estadística

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In this paper, the Bayesian analysis of the survival data arising from a Rayleigh model is carried out under the assumption that the clinical study based on n patients is terminated at the d death, for some preassigned d (0 < d ≤ n), resulting in the survival times t ≤ t ≤ ... ≤ t, and (n - d) survivors. For the prior knowledge about the Rayleigh parameter, the gamma density, the inverted gamma density, and the beta density of the second kind are respectively assumed, and for...

Change-point problems: A Bayesian nonparametric approach

Pietro Muliere, Marco Scarsini (1985)

Aplikace matematiky

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A change-point problem is examined from a Bayesian viewpoint, under nonparametric hypotheses. A Ferguson-Dirichlet prior is chosen and the posterior distribution is computed for the change-point and for the unknown distribution functions.

Objective Bayesian point and region estimation in location-scale models.

José M. Bernardo (2007)

SORT

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Point and region estimation may both be described as specific decision problems. In point estimation, the action space is the set of possible values of the quantity on interest; in region estimation, the action space is the set of its possible credible regions. Foundations dictate that the solution to these decision problems must depend on both the utility function and the prior distribution. Estimators intended for general use should surely be invariant under one-to-one transformations,...

Pivotal inference and the Bayesian controversy.

George A. Barnard (1980)

Trabajos de Estadística e Investigación Operativa

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The theory of pivotal inference applies when parameters are defined by reference to their effect on observations rather than their effect on distributions. It is shown that pivotal inference embraces both Bayesian and frequentist reasoning.

Likelihood and the Bayes procedure.

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.