Bayesian networks are graphical models that represent the joint distributionof a set of variables using directed acyclic graphs. When the dependence structure is unknown (or partially known) the network can be learnt from data. In this paper, we propose a constraint-based method to perform...

Quality management and customer satisfaction evaluation can be difficult tasks to perform when processes involve multiple production lines or provide multichannel services. As a consequence, the top management needs to check the quality from different perspectives and to evaluate the improvement...

In this paper we consider the problem of identifying an autoregressive model for an observed time series and detecting a possible unit root in its characteristic polynomial. This is a big issue concerned with distinguishing stationary time series from time series for which differencing is...

Bayesian hypothesis testing for non-nested hypotheses various "default" Bayes factors, such as the fractional Bayes factor, the median intrinsic Bayes factor and the encompassing and expected intrinsic Bayes factors. The different default methods are first compared with each other and with the...