Finding the “right” number of clusters, Ic, for a data set is a difficult, and often ill-posed, problem. In a probabilistic clustering context, likelihood-ratios, penalized likelihoods, and Bayesian techniques are among the more popular techniques. In this paper a new cross-validated likelihood criterion is investigated for determining cluster structure. A practical clustering algorithm based o...

Many statistical models of interest to the natural and social sciences have no tractable likelihood function. Until recently, Bayesian inference for such models was thought infeasible. Pritchard et al. (1999) introduced an algorithm known as ABC, for Approximate Bayesian Computation, that enables Bayesian computation in such models. Despite steady progress since this rst breakthrough, such as t...

One of the main limitations in predictive analytics is the acquisition cost of engineering data due to slow-running computer code or expensive experiments. Also, data is often multi-dimensional and highly non-linear in nature, causing problems for standard statistical predictive models. Once data is collected and models are built, many applications require accurate and scalable uncertainty quan...

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect ...

Two approaches have been used to perform approximate inference in Bayesian networks for which exact inference is infeasible: employing an approximation algorithm, or approximating the structure. In this article we compare two structure-approximation techniques, edge-deletion and approximate structure learning based on sub-sampling, in terms of relative accuracy and computational efficiency. Our...

In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predicting student performance. Additionally, we study the importance of including students’ attempt time sequences of parameterized exercises. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Bayesian Probabilistic Tensor Factorization...

the problems of sequential change-point have several important applications in quality control, signal processing, and failure detection in industry and finance. we discuss a bayesian approach in the context of statistical process control: at an unknown time $tau$, the process behavior changes and the distribution of the data changes from p0 to p1. two cases are considered: (i) p0 and p1 are fu...

A number of brain imaging techniques have been developed in order to investigate brain function and to develop diagnostic tools for various brain disorders. Each modality has strengths as well as weaknesses compared to the others. Recent work has explored how multiple modalities can be integrated effectively so that they complement one another while maintaining their individual strengths. Bayes...

Deep learning techniques are used more and more often, but they lack the ability to reason about uncertainty over the features. Features extracted from a dataset are given as point estimates, and do not capture how much the model is confident in its estimation. This is in contrast to probabilistic Bayesian models, which allow reasoning about model confidence, but often with the price of diminis...

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively spec...