نتایج جستجو برای: jeffreys

تعداد نتایج: 514  

2002
Mihaela Aslan M. ASLAN

fθ log (fθ/f̂) is used to examine various ways of choosing prior distributions; the principal type of choice studied is minimax. We seek asymptotically least favorable predictive densities for which the corresponding asymptotic risk is minimax. A result resembling Stein’s paradox for estimating normal means by the maximum likelihood holds for the uniform prior in the multivariate location family...

2009
William D. Sudderth

In 1946, Sir Harold Jeffreys introduced a prior distribution whose density is the square root of the determinant of Fisher information. The motivation for suggesting this prior distribution is that the method results in a posterior that is invariant under reparametrization. For invariant statistical models when there is a transitive group action on the parameter space, it is shown that all rela...

2013
Cuirong Ren Dongchu Sun

Abstract Objective priors, especially reference priors, have been studied extensively for spatial data in the last decade. In this paper, we study objective priors for a CAR model. In particular, the properties of the reference prior and the corresponding posterior are studied. Furthermore, we show that the frequentist coverage probabilities of posterior credible intervals depend only on the sp...

2001
Malay GHOSH Yeong-Hwa KIM

The Behrens-Fisher problem concerns the inference for the difference between the means of two normal populations whose ratio of variances is unknown. In this situation, Fisher’s fiducial interval differs markedly from the Neyman-Pearson confidence interval. A prior proposed by Jeffreys leads to a credible interval that is equivalent to Fisher’s solution, but carries a different interpretation. ...

2017
Eric T. Nalisnick Padhraic Smyth

Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective priors. However, objective priors such as the Jeffreys and reference priors are not tractable to derive for many models of interest. We address this issue by proposing techniques for learning reference prior approximations: we select a parametric family and ...

2008
Christian P. Robert Nicolas Chopin Judith Rousseau

Published nearly seventy years ago, Jeffreys’ Theory of Probability (1939) has had a unique impact on the Bayesian community and is now considered to be one of the main classics in Bayesian Statistics as well as the initiator of the objective Bayes school. In particular, its advances on the derivation of noninformative priors as well as on the scaling of Bayes factors have had a lasting impact ...

Journal: :Applicationes Mathematicae 2001

Journal: :International Statistical Review 2006

2010
Esther Salazar Marco A. R. Ferreira Helio S. Migon

We develop objective Bayesian analysis for the linear regression model with random errors distributed according to the exponential power distribution. More specifically, we derive explicit expressions for three different Jeffreys priors for the model parameters. We show that only one of these Jeffreys priors leads to a proper posterior distribution. In addition, we develop fast posterior analys...

1998
Jun-ichi Takeuchi Andrew R. Barron

We introduce a notion of ‘relative redundancy’ for universal data compression and propose a universal code which asymptotically achieves the minimax value of the relative redundancy. The relative redundancy is a hybrid of redundancy and coding regret (pointwise redundancy), where a class of information sources and a class of codes are assumed. The minimax code for relative redundancy is an exte...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید