There are many methods of scoring the importance variables in prediction a response but not much is known about their accuracy. This paper partially fills gap by introducing new method based on GUIDE algorithm and comparing it with 11 existing methods. For data without missing values, eight shown to give biased scores that too high or low, depending type (ordinal, binary nominal) whether they d...

In this paper, we consider the coherent theory of (epistemic) uncertainty of Walley, in which beliefs are represented through sets of probability distributions, and we focus on the problem of modeling prior ignorance about a categorical random variable. In this setting, it is a known result that a state of prior ignorance is not compatible with learning. To overcome this problem, another state ...

Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating, and adapt to changes or drifts in the underlying data generating distribution. In this paper, we approach these problems from a Bayesian perspective covering general conjugate exponential models. Our proposal makes use of non-...

Uncertainty theory is a branchof axiomaticmathematics formodelingbelief degrees. This theory provides a new mathematical tool for indeterminacy phenomena. Uncertain variable is a fundamental concept in uncertainty theory and used to represent quantities with uncertainty. There are some important characteristics about uncertain variables. The expected value of uncertain variable is its average v...

The purpose of the current review is to examine individual differences in i ntelligence and working memory capacity. The emphasis is on latent variable models and theoretical frameworks that connect interindividual differences in behavior with intraindividual psychological processes. Our review suggests that intelligence and working memory capacity are strongly correlated and that the shared va...

Learning a Bayesian network from data is a model specific task, and thus requires careful consideration of contextual information, namely, contextual independencies. In this paper, we study the role of hidden variables in learning causal models from data. We show how statistical methods can help us discover these hidden variables. We suggest hidden variables are wrongly ignored in inference, be...

1. Proofs In this section we derive the proofs of all propositions in the main paper. Proposition 1. The AD entropy of the generalized distribution of y can be written as the sum of the negative log-likelihood of y and the AD entropy of the conditional distribution of the hidden variable given the output, Hα,β(Q y x ;w) = − logP (y|x,w) +Hα,β(P y x ;w). (1) Proof. The AD entropy of the generali...

Motivated by the large number of languages (seven) and the short development time (two months) of the 2009 CoNLL shared task, we exploited latent variables to avoid the costly process of hand-crafted feature engineering, allowing the latent variables to induce features from the data. We took a pre-existing generative latent variable model of joint syntacticsemantic dependency parsing, developed...