The naive Bayes classifier greatly simplify learning by assuming that features are independent given class. Although independence is generally a poor assumption, in practice naive Bayes often competes well with more sophisticated classifiers. Our broad goal is to understand the data characteristics which affect the performance of naive Bayes. Our approach uses Monte Carlo simulations that allow...

Rough set theory is a new approach to vagueness and uncertainty. The theory of rough sets has an overlap with many other theories. Specially interesting is the relationship to fuzzy set theory and the theory of evidence. Recently, it turned out that the theory has very interesting connections with Bayes’ theorem. The look on Bayes’ theorem offered by rough set theory reveals that any data set (...

This paper gives an algebraic derivation of the posterior for both the noisy-or and naive Bayes models, as a function of both input messages and probability table parameters. By examining these functions we show a technique where the naive Bayes model may be used to approximate a logical-OR, rather than its typical interpretation as a logicalAND. The technique is to avoid the use of disconfirmi...

Bayes factors have been advocated as superior to p-values for assessing statistical evidence in data. Despite the advantages of Bayes factors and the drawbacks of p-values, inference by p-values is still nearly ubiquitous. One impediment to the adoption of Bayes factors is a lack of practical development, particularly a lack of ready-to-use formulas and algorithms. In this paper, we discuss and...

Software bug classification is a precondition for bug fixation and it plays a vital role in software maintenance. It is found that bug fixation often takes long due to the distribution of misclassified or non-classified bugs by the triager among the developers. In this paper, we propose an adaptive bug classification approach on CVE dataset that involves two Bayesian classifiers such as Naive B...

today a significant part of available data is saved in text database or text documents. the most important thing is to organize these documents. one way to organize text documents is to classify them. to classify texts is to assign text documents to their actual categories. this has two main steps, i.e. feature- and learning algorithm selection. there have been several methods suggested to clas...

Recently substantial research in artificial intelligence (AI) has focused on Bayes nets and the closely related influence diagrams. However, there has been little work in the accounting and auditing communi­ ties in this area of AI research. Bayes nets and influence diagrams have some advantages over deterministic AI approaches. These approaches employ probabilities to model decision making. Pr...

We consider a model in which there are m products and for each i=1,...,m, there are ni observations of the service times associated with the th i product. Assuming the service times associated with the th i product follow an exponential distribution with parameter i θ , and i θ 's are independent random variables with a common gamma distribution, with unknown parameters α and β , we provide two...

We discuss the implications of Bayes’ postulate in the setting of exchangeable Bernoulli random variables. Bayes’ postulate, here, stipulates a uniform distribution on the total number of successes in any number of trials. For an infinite sequence of exchangeable Bernoulli variables the conditions of Bayes’ postulate are equivalent to a uniform (prior) distribution on the underlying mixing vari...