BACKGROUND AND AIMS It has been proposed that more use should be made of Bayes factors in hypothesis testing in addiction research. Bayes factors are the ratios of the likelihood of a specified hypothesis (e.g. an intervention effect within a given range) to another hypothesis (e.g. no effect). They are particularly important for differentiating lack of strong evidence for an effect and evidenc...

Naive Bayes The multinomial Naive Bayes model on a dictionary is a familiar option for text classification, e.g. (Gale, Church, & Yarowski 1992), (McCallum & Nigam 1998). When there are additional features, the Naive Bayes model has also a natural extension: We simply assume that each additional feature is independent of all the others, conditional upon . In this case, we invert Bayes’ Law by o...

This paper mainly deals with various classification algorithms namely, Bayes. NaiveBayes, Bayes. BayesNet, Bayes. NaiveBayesUpdatable, J48, Randomforest, and Multi Layer Perceptron. It analyzes the hepatitis patients from the UC Irvine machine learning repository. The results of the classification model are accuracy and time. Finally, it concludes that the Naive Bayes performance is better than...

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with assumptions of conditional independence among features given the class, called naı̈ve Bayes, is competitive with state of the art classifiers. On this paper a new naive Bayes classifier called Interval Estimation naı̈ve Bayes is proposed. Interval Estimation naı̈ve Bayes performs on two phases. On the ...

Wheeler WC and Pickett KM (2008. Topology-Bayes versus clade-Bayes in phylogenetic analysis. Mol Biol Evol. 25:447–453.) discuss two ways of summarizing the posterior probability distribution of a Bayesian phylogenetic analysis, which they refer to as ‘‘topology-Bayes’’ and ‘‘clade-Bayes.’’ They claim that the clade-Bayes approach leads to problems such as ‘‘exaggerated clade support, inconsist...

the problem of estimating the parameter ?, when it is restricted to an interval of the form , in a class of discrete distributions, including binomial negative binomial discrete weibull and etc., is considered. we give necessary and sufficient conditions for which the bayes estimator of with respect to a two points boundary supported prior is minimax under squared log error loss function. for s...

One-class Bayes learning such as one-class Naïve Bayes and one-class Bayesian Network employs Bayes learning to build a classifier on the positive class only for discriminating the positive class and the negative class. It has been applied to anomaly detection for identifying abnormal behaviors that deviate from normal behaviors. Because one-class Bayes classifiers can produce probability score...