In this paper, we discuss different predictors of times to failure of units censored in a hybrid censored sample from exponential distribution. Bayesian and non-Bayesian point predictors for the times to failure of units are obtained. Non-Bayesian prediction Intervals are obtained based on pivotal and highest conditional density methods. Bayesian prediction intervals are also proposed. One real...

  Background: Osteonecrosis (ON) is known to be one of the most disabling complications following corticosteroid (CS) medications. However, evidence regarding risk of asymptomatic prevalence of ON among different diseases and the impact of variable steroid regimens are conflicting. We aimed to determine the prevalence of ON of femoral head in asymptomatic patients with systemic rheumatic diseas...

Context-sensitive dependency pairs (CS-DPs) are currently the most powerful method for automated termination analysis of contextsensitive rewriting. However, compared to DPs for ordinary rewriting, CS-DPs suffer from two main drawbacks: (a) CS-DPs can be collapsing. This complicates the handling of CS-DPs and makes them less powerful in practice. (b) There does not exist a “DP framework” for CS...

production of lignocellulolytic enzymes by the thermotolerant fomes sp. eum1 was determined in solid cultures using corn stover (cs) as a sole substrate or supplemented with 20 % wheat bran (cs+wb). this supplementation increased (p< 0.05) enzymatic activity per gram of initial dry matter (gdm) for xylanases and cellulases: 160 iu g dm-1 and 37 iu g dm-1, respectively; while laccases reached a ...

Code-switching (CS) between an Indian language and (Indian) English is, and has long been, a normal feature of everyday speech in urban Indian society. Although much has been written about the status and role of English, and about the sociological variables associated with English usage in India, there have been, to date, no studies explicitly investigating changes in CS patterns over time. Bol...

As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modelling uncertainty is one of the key features of Bayesian methods. Using Bernoulli dropout with sampling at prediction time has recently been proposed as an efficient and well performing variational inference method for DNNs. However, sampling from ot...

We give a new consistent scoring function for structure learning of Bayesian networks. In contrast to traditional approaches to score-based structure learning, such as BDeu or MDL, the complexity penalty that we propose is data-dependent and is given by the probability that a conditional independence test correctly shows that an edge cannot exist. What really distinguishes this new scoring func...

Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides theoretically optimal predictions and is known to be robust to overfitting. In this work we introd...