The Geometric Theory of Ignorance produces posterior distributions from priors and likelihoods without invoking Bayes Theorem at all. The standard bayesian posteriors minimize the ignorance action with parameters δ = ν = 0 when the truth t is replaced by the empirical distribution of n independent observations. Different values for the parameters produce new ways for processing the data obtaini...

This paper considers estimation of normal mean ? when the variance is unknown, using the LINEX loss function. The unique Bayes estimate of ? is obtained when the precision parameter has an Inverse Gaussian prior density

 Traditional regression analyses assume normality of observations and independence of mean and variance. However, there are many examples in science and Technology where the observations come from a skewed distribution and moreover there is a functional dependence between variance and mean. In this article, we propose a method for regression analysis under Inverse Gaussian model when th...

Multi-label classification is one of the important research areas in data mining. In this paper, a new multilabel classification method using multinomial naive Bayes is proposed. We use a new fine-grained weighting method for calculating the weights of feature values in multinomial naive Bayes. Our experiments show that the value weighting method could improve the performance of multinomial nai...

We propose an approach to post-election auditing based on Bayesian principles, and give experimental evidence for its efficiency and effectiveness. We call such an audit a “Bayes audit”. It aims to control the probability of miscertification (certifying a wrong election outcome). The miscertification probability is computed using a Bayesian model based on information gathered by the audit so fa...

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...