As Bayesian networks are applied to more complex and realistic real-world applications, the development of more efficient inference approaches is increasingly important. This paper presents a method for metareasoning in Bayesian networks adopting prediction models to select algorithms for the inference tasks, when multiple schemes are used to calculate the propagation of evidence. The proposed ...

We characterize the high-order coverage accuracy of smoothed and unsmoothed Bayesian bootstrap confidence intervals for population quantiles. Although original (Rubin 1981) have same O(n−1/2) error as standard empirical bootstrap, Banks (1988) has much smaller O(n−3/2[log(n)]3) is exact in special cases, without requiring any smoothing parameter. It automatically removes an term order 1/n that ...

This paper proposes the use of prediction intervals based on bootstrap for threshold autoregressive models. We consider four bootstrap methods to account for the variability of estimated threshold values, correct the bias of autoregressive coefficients and allow for heterogenous errors. Simulation shows that bootstrap prediction intervals generally perform better than classical prediction inter...

Bayesian methods for inference on finite population means and other parameters by using sample survey data face hurdles in all three phases of the inferential procedure: the formulation of a likelihood function, the choice of a prior distribution and the validity of posterior inferences under the design-based frequentist framework. In the case of independent and identically distributed observat...

in recent years, decrease in depth of uromia lake, has resulted in higher increase of salinity threat in agricultural lands around the lake. the aims of this study were 1- investigation of the spatial changes in soil salinity using the bayesian maximum entropy method (bme); 2- prediction of the boundary between saline and agricultural lands; and 3- assessment of the uncertainty involved with sa...

Abstract The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical methods. Model based methods require knowledge of the model and the underlying innovation distribution. Empirical methods are based on the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical forecast errors using lead t...

In order to perform Preventive Maintenance (PM), two approaches have evolved in the literature. The traditional approach is based on the use of statistical and reliability analysis of equipment failure. Under statistical-reliability (S-R)-based PM, the objective of achieving the minimum total cost is pursued by establishing fixed PM intervals, which are statistically optimal, at which to replac...