نتایج جستجو برای: prior distribution

تعداد نتایج: 835108  

Journal: :Biometrics 2017
Leonhard Held Rafael Sauter

The prior distribution is a key ingredient in Bayesian inference. Prior information on regression coefficients may come from different sources and may or may not be in conflict with the observed data. Various methods have been proposed to quantify a potential prior-data conflict, such as Box's p-value. However, there are no clear recommendations how to react to possible prior-data conflict in g...

Journal: :CoRR 2017
Eirikur Agustsson Alexander Sage Radu Timofte Luc Van Gool

Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian. After a trained model is obtained, one can sample the Generator in various forms for exploration and understanding, such as interpolating between two samples, sampling in the vicinity of a sam...

2008
S. Bayarri I. Carbonell L. Izquierdo A. Tárrega

Discrimination rates of panellists performing replicated difference tests are estimated in the present paper according to Bayes’ rule by considering the successive replications as different steps and using the posterior distribution obtained in each step as prior distribution of the following step. Data are also successively obtained in real situations and, thus, this approach imitates what hap...

In this paper, the reconcilability between the P-value and the posterior probability in testing a point null hypothesis against the one-sided hypothesis is considered. Two essential families, non regular and exponential family of distributions, are studied. It was shown in a non regular family of distributions; in some cases, it is possible to find a prior distribution function under which P-va...

2018
Danielle L Burke Sylwia Bujkiewicz Richard D Riley

Multivariate random-effects meta-analysis allows the joint synthesis of correlated results from multiple studies, for example, for multiple outcomes or multiple treatment groups. In a Bayesian univariate meta-analysis of one endpoint, the importance of specifying a sensible prior distribution for the between-study variance is well understood. However, in multivariate meta-analysis, there is lit...

Journal: :Statistical theory and related fields 2022

The Lomax distribution is an important member in the family. In this paper, we systematically develop objective Bayesian analysis of data from a distribution. Noninformative priors, including probability matching maximal information (MDI) prior, Jeffreys prior and reference are derived. propriety posterior under each subsequently validated. It revealed that MDI one priors yield improper posteri...

2004
P. MULIERE P. SECCHI

Abst rac t . We address the question as to whether a prior distribution on the space of distribution functions exists which generates the posterior produced by Efron's and Rubin's bootstrap techniques, emphasizing the connections with the Dirichlet process. We also introduce a new resampling plan which has two advantages: prior opinions are taken into account and the predictive distribution of ...

2005
Xikui Wang Yanqing YI Kaiji Liao

We consider the optimal investment-consumption problem with an unknown distribution for the investment returns. After taking the Bayesian approach, the problem is formulated as a Markov decision process in a nonparametric context. We discuss the existence of the optimal strategy and derive the closed form solution of the optimal portfolio. When the prior distribution is a Dirichlet process, the...

2016
Fabrizio Leisen Luca Rossini Cristiano Villa

The Yule–Simon distribution is usually employed in the analysis of frequency data. As the Bayesian literature, so far, ignored this distribution, here we show the derivation of two objective priors for the parameter of the Yule–Simon distribution. In particular, we discuss the Jeffreys prior and a loss-based prior, which has recently appeared in the literature. We illustrate the performance of ...

Journal: :CoRR 2017
Bart Jacobs

A desired closure property in Bayesian probability is that an updated posterior distribution is in the same class of distributions — say Gaussians — as the prior distribution. When the updating takes place via a likelihood, one then calls the class of prior distributions the ‘conjugate prior’ of this likelihood. This paper gives (1) an abstract formulation of this notion of conjugate prior, usi...

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