Naive Bayes algorithm captures the assumption that every attribute is independent from the rest of the attributes, given the state of the class attribute. In this study, we attempted to increase the prediction accuracy of the simple Bayes model by integrating global and local application of Naive Bayes classifier. We performed a large-scale comparison with other attempts that have tried to impr...

Progress in science often comes from discovering invariances in the relationship between variables. Invariances correspond to null hypotheses. As is commonly known, it is not possible to accumulate evidence for the null hypothesis in conventional significance testing. We highlight a Bayes factor alternative to the t-test for accumulating evidence for either the null or alternative hypotheses. T...

Naïve Bayes classifiers are simple probabilistic classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of str...

MOTTO: " It is a capital mistake to theorise before one has data " Sherlock Holms In: A Scandal in Bohemia Abstract. Rough set theory offers new perspective on Bayes' theorem. The look on Bayes' theorem offered by rough set theory reveals that any data set (decision table) satisfies total probability theorem and Bayes' theorem. These properties can be used directly to draw conclusions from obje...

Rough set theory offers new perspective on Bayes’ theorem. The look on Bayes’ theorem offered by rough set theory reveals that any data set (decision table) satisfies the total probability theorem and Bayes’ theorem. These properties can be used directly to draw conclusions from objective data without referring to subjective prior knowledge and its revision if new evidence is available. Thus, t...

We consider the problem of forecasting multi-class job flow times in a resource-sharing environment. We assume the deviation of flow times in each class from the class nominal value follows an exponential distribution with its parameter following a gamma distribution. A large simulation experiment is conducted to assess and compare the performance of the Bayes and empirical Bayes forecasting me...

This paper introduces estimations of reliabilities index for Rayleigh components in a series system using Type-II censored and masked system life data. We derive the Maximum likelihood estimation (MLE) of the unknown parameters for Rayleigh components and reliabilities index. In view of the limitation of MLE under complete masked case, we employ the Bayes approach. Bayes estimations of the Rayl...

Bayes linear methodology provides a quantitative structure for expressing our beliefs and systematic methods for revising these beliefs given observational data. Particular emphasis is placed upon interpretation of and diagnostics for the specification. The approach is similar in spirit to the standard Bayes analysis, but is constructed so as to avoid much of the burden of specification and com...

The Bayes factor is commonly used for assessing the evidence for or against a given hypothesis H0 : θ ∈ Θ0, where Θ0 is a subset of the parameter space. In this paper we discuss the Bayes factor and various issues associated with its use. A Bayes factor is seen to be intimately connected with a relative belief ratio which provides a somewhat simpler approach to assessing the evidence in favor o...