We present new techniques for the application of a Bayesian network learning framework to the problem of classifying gene expression data. The focus on classification permits us to develop techniques that address in several ways the complexities of learning Bayesian nets. Our classification model reduces the Bayesian network learning problem to the problem of learning multiple subnetworks, each...

This tutorial demonstrates the usage of BayesX for analysing Bayesian semiparametric regression models based on mixed model methodology. As an example we consider data on undernutrition of children in Zambia. The tutorial is designed to be self-contained and describes all features of BayesX in detail, that will be needed throughout the tutorial. Therefore it may also serve as a first introducti...

The emergence of Bayesian methodology has facilitated respondent-level conjoint models, and deriving utilities from choice experiments has become very popular among those modeling product line decisions or new product ∗ Co-chairs. Author order is alphabetical.

The aim of the study is an uncertainty analysis of an air dispersion model[ The model used is described in NRPB!R80 "Clarke\ 0868#\ a model for short and medium range dispersion of radionuclides released into the atmosphere[ Uncertainties in the model predictions arise both from the uncertainty of the input variables and the model simpli_cations\ resulting in parameter uncertainty[ The uncertai...

Classification is an important data mining technique that is used by many applications. Several types of classifiers have been described in the research literature. Example classifiers are decision tree classifiers, rule-based classifiers, and neural networks classifiers. Another popular classification technique is naïve Bayesian classification. Naïve Bayesian classification is a probabilistic ...

acceptance sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. a number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. the acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are acco...

Acceptance Sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. A number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. The acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are acco...

In this paper we propose a new methodology for link-based document classification based on probabilistic classifiers and Bayesian networks. We also report the results obtained of its application to the XML Document Mining Track of INEX’09.

In the following paper we investigate simulation methodology for Bayesian inference in Lévy driven SV models. Typically, Bayesian inference from such statistical models is performed using Markov chain Monte Carlo (MCMC) methods. However, it is well-known that fitting SV models using MCMC is not always straight-forward. One method that can improve over MCMC is SMC samplers ([14]), but in that ap...

We consider the problem of multiband image clustering and segmentation. We propose a new methodology for doing this, called modelbased cluster trees. This is grounded in model-based clustering, which bases inference on finite mixture models estimated by maximum likelihood using the EM algorithm, and automatically chooses the number of clusters by Bayesian model selection, approximated using BIC...