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Displaying 81 –
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Predictive sample reuse methods usually applied in low structure aparametric paradigms are shown to be useful in certain high structure situations when conjoined with a Bayesian approach. Particular attention is focused on the incomplete data situation for which two alternative sample reuse approaches are devised. The first involves differential weighting and the second a recursive sample reuse algorithm. These are applied to censored exponential survival data. The exponential approach appears to...
En lo que sigue, abordaremos problemas de decisión en ambiente de riesgo y con experimentación, en donde la distribución de probabilidad a priori sobre los estados de la Naturaleza no es perfectamente conocida, sino que solamente se posee una información cualitativa de la misma. Más concretamente, dados dos estados de la Naturaleza cualesquiera, se conoce a lo más, cuál de ellos es más probable que el otro, si bien no se tiene una idea cuantitativa de esta diferencia de probabilidades.
In this paper the theoretical and practical implications of dropping -from the basic Bayesian coherence principles- the assumption of comparability of every pair of acts is examined. The resulting theory is shown to be still perfectly coherent and has Bayesian theory as a particular case. In particular we question the need of weakening or ruling out some of the axioms that constitute the coherence principles; what are their practical implications; how this drive to the notion of partial information...
The problem of robust Bayesian estimation in some models with an asymmetric loss function (LINEX) is considered. Some uncertainty about the prior is assumed by introducing two classes of priors. The most robust and conditional Γ-minimax estimators are constructed. The situations when those estimators coincide are presented.
La idea de casi-admisibilidad, esto es, admisibilidad salvo conjuntos de medida nula, se extiende a situaciones más generales que las hasta ahora consideradas. Se estudia el problema de su existencia y la relación con una subclase de las reglas de Bayes; en particular su relación con la regla de Cromwell. La idea de soporte de una distribución se extiende a esta nueva situación y se relaciona con el concepto clásico de soporte y otros conceptos como el de regularidad.
Estudiamos los modelos jerárquicos en relación con el problema de decisión con información parcial, es decir, en el caso en que dicha información a priori venga representada en etapas sucesivas. Damos una serie de resultados, basándonos en una expresión generalizada del teorema de Bayes (De Robertis-Hartigan, 1981).
Two concepts of optimality corresponding to Bayesian robust analysis are considered: conditional Γ-minimaxity and stability. Conditions for coincidence of optimal decisions of both kinds are stated.
Axioms are given for positive comparative probabilities and plausibilities defined either on Boolean algebras or on arbitrary sets of events. These axioms allow to characterize binary relations representable by either standard or nonstandard measures (i. e. taking values either on the real field or on a hyperreal field). We also study relations between conditional events induced by preferences on conditional acts.
A Bayesian method of estimation of a success probability p is considered in the case when two experiments are available: individual Bernoulli (p) trials-the p-experiment-or products of r individual Bernoulli (p) trials-the -experiment. This problem has its roots in reliability, where one can test either single components or a system of r identical components. One of the problems considered is to find the degree r̃ of the -experiment and the size m̃ of the p-experiment such that the Bayes estimator...
The problem of estimating the mean of a normal distribution is considered in the special case when the data arrive at random times. Certain classes of Bayes sequential estimation procedures are derived under LINEX and reflected normal loss function and with the observation cost determined by a function of the stopping time and the number of observations up to this time.
Where a decision-maker has to rely on expert opinions a need for a normative model to combine these forecasts appears. This can be done using Bayes' formula and by making some assumptions on the prior distribution and the distribution of the expert assessments. We extend the case to skewed distributions of these assessments. By using an Edgeworth expansion of the density function including the skewness parameter, we are able to obtain the formula to combine the forecasts in a Bayesian way.
The sum-product algorithm is a well-known procedure for marginalizing an “acyclic” product function whose range is the ground set of a commutative semiring. The algorithm is general enough to include as special cases several classical algorithms developed in information theory and probability theory. We present four results. First, using the sum-product algorithm we show that the variable sets involved in an acyclic factorization satisfy a relation that is a natural generalization of probability-theoretic...
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