Agostinelli C, Greco L (2013) A weighted strategy to handle likelihood uncertainty in bayesian inference. Comput Stat 28:319-339Agostinelli, C. and Greco, L. (2013). A weighted strategy to handle likelihood uncertainty in Bayesian inference. Computational Statistics 28 319-339....
Bayesian inference and diagnostics for the three-parameter logistic model (ONR Technical Report 85-5). Iowa City, IA : CADA Research Group, University ... T Leonard,MR Novick 被引量: 10发表: 1985年 Prediction model for outcome after low-back surgery: individualized likelihood of complication,...
Statistical Data Fusion || Bayesian Inference for Weighted Systems of Distributions The following sections are included:IntroductionSimple Weighted Systems of DistributionsLikelihoodPriorPosterior SimulationBayesian InferenceEstimationExample: Radar MeteorologyTest of HypothesesExample: Radar Meteorology (Continuation)D...
The empirical likelihood method first proposed by Owen (1988) is a powerful nonparametric inference tool with applications in many areas of statistics. New development is still active trying to extend the method to handle various non-regular situations. Owen (2001) provides a comprehensive account an...
It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights ...
point of the shape in shape classifications. Hence, we introduce a new discriminant function which weights the curvature likelihood of shape points according to their importance in classification. The new discriminant function, g j is given by, g j = T t=1 w j (t).Υ t,j , (12...
Prediction for future failures in Weibull distribution under hybrid censoring A. Asgharzadeh, et al. 2013 Bayesian and maximum likelihood estimations of the inverse Weibull parameters under progressive type-II censoring K. S. Sultan, et al. Volume 84, Issue 10, 2014 View disclaimer for citi...
Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in terms of their ability to match the true posterior distribution. In turn, the expressivity of the variational family is la...
In CWNB, we first learn the weight of each training instance iteratively based on its estimated posterior probability loss to make the prior and conditional probabilities more accurate, then we incorporate these two probabilities into the conditional log-likelihood (CLL) formula, and at last we ...
The check loss function in quantile regression is adjusted to be quadratic near its minimum. This modification produces an averaging effect near the target... Jung, Yoonsuh. 被引量: 3发表: 2010年 Maximum likelihood optimal and robust Support Vector Regression with lncosh loss function. Most of...