This package installs and interfaces the naive Bayesian classifier for 16S rRNA sequences developed by the Ribosomal Database Project (RDP). With this package the classifier trained with the standard training set can be used or a custom classifier can be trained.

1 Naive Bayes, 20 points Problem 1. Basic concepts, 10 points Naive Bayes reduces the number of parameters that must be estimated for a Bayesian classifier, by making a conditional independence assumption when modeling P (X|Y). The definition for conditional independence is the following: Given this definition, please answer the following questions:

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...

The rule conflict is an important issue for associative classification due to a large set of rules. In this paper, a new approach called Associative Classification with Bayes (AC-Bayes) is proposed. To address rule conflicts, AC-Bayes has two distinguished features: (1) Associative classification is improved. (2) Naïve Bayesian model is applied in process of classification. A small set of high ...

Bayesian Networks are one of the most popular formalisms for reasoning under uncertainty. Hierarchical Bayesian Networks (HBNs) are an extension of Bayesian Networks that are able to deal with structured domains, using knowledge about the structure of the data to introduce a bias that can contribute to improving inference and learning methods. In effect, nodes in an HBN are (possibly nested) ag...

The paper presents an approach to joint tracking and classification based on belief functions as understood in the transferable belief model (TBM). For the tracking phase, a Kalman filter in the TBM framework is derived. This filter is essentially the same as the classical Kalman filter with a diffuse prior, although it is derived in a more general context. For the classification phase, the TBM...

Hierarchical Bayes and Empirical Bayes are related by their goals, but quite different by the methods of how these goals are achieved. The attribute hierarchical refers mostly to the modeling strategy, while empirical is referring to the methodology. Both methods are concerned in specifying the distribution at prior level, hierarchical via Bayes inference involving additional degrees of hierarc...

Bayes nets are relatively recent innovations. As a result, most of their theoretical devel­ opment has focused on the simplest class of single-author models. The introduction of more sophisticated multiple-author set­ tings raises a variety of interesting ques­ tions. One such question involves the na­ ture of compromise and consensus. Poste­ rior compromises let each model process all data to ...

Based on the notion of predictive influence functions, the paper develops multivariate limited translation hierarchical Bayes estimators of the normal mean vector which serve as a compromise between the hierarchical Bayes and maximum likelihood estimators. The paper demonstrates the superiority of the limited translation estimators over the usual hierarchical Bayes estimators in terms of the fr...

Flight delay creates major problems in the current aviation system. Methods are needed to analyze the manner in which micro-level causes propagate to create system-level patterns of delay. Traditional statistical methods are inadequate to the task. This paper proposes the use of Bayesian networks (BNs) to investigate and visualize propagation of delays among airports. The BN structure was devel...