Abstract Bayes estimators of the scale parameter of a generalized gamma type model are obtained for different priors. For the proposed prior, Bayes estimator of θ for given p and k=1 coincides with MLE of θ if c=2, whereas Bayes estimator of hazard rate function of θ given p and k coincides with its MLE. Thumb’s rule has been used for constructing a conjugate prior for θ . Bayes estimators of r...

Based on progressively Type-II censored samples, the maximum likelihood and Bayes estimators for the scale parameter, reliability and cumulative hazard functions are derived. The Bayes estimators are studied under symmetric (squared error) loss function and asymmetric (LINEX and general entropy) loss functions. Tow techniques are used for computing the Bayes estimates; standard Bayes and import...

conclusions the results of the present study indicate that brazilian propolis demonstrated in vitro anti-microbial efficacy against s. mutans and c. albicans. results the antimicrobial efficacy of hexidine against s. mutans and e. coli was higher compared to that of c. albicans. the brazilian propolis (p) mr showed antimicrobial action against s. mutans and c. albicans. the antimicrobial effica...

background: mr imaging is one of the best diagnostic modalities in medicine. during an mr imaging examination, three types of field are employed to produce images. various experimental studies have been performed about the effects of each single type of field but only few studies are available on their combination to generate mr imaging. the main objective of this research work is to study the ...

This paper presents a new Bayes classification rule towards minimizing the predictive Bayes risk for robust speech recognition. Conventionally, the plug-in maximum a posteriori (MAP) classification is constructed by adopting nonparametric loss function and deterministic model parameters. Speech recognition performance is limited due to the environmental mismatch and the ill-posed model. Concern...

The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness—the assumption that attributes are independent given the class. All of them improve the performance of naive Bayes at the expense (to a greater or lesser d...

Naive Bayes is a well-known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The Iterative Bayes begins with the distribution tables built by the naive Bayes. Those tables are iteratively updated in order to improve the probability...

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we examine and evaluate approaches for ...

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we examine and evaluate approaches for ...