A large collection of estimation phenomena (e.g. biases arising when adults or children estimate remembered locations of objects in bounded spaces; Huttenlocher, Newcombe & Sandberg, 1994) are commonly explained in terms of complex Bayesian models. We provide evidence that some of these phenomena may be modeled instead by a simpler non-Bayesian alternative. Undergraduates and 9to 10-year-olds c...

A simple weighting scheme for atomic refinement is discussed. The approach, called 'Bayesian weighting', is designed to be robust with respect to the bias that arises from the incomplete nature of the atomic model, which in macromolecular crystallography is typically quite serious. Bayesian weights are based on the mean-squared residual errors over shells of resolution, with centric and acentri...

Variational Bayesian learning is an approximation to the exact Bayesian learning where the true posterior is approximated with a simpler distribution. In this paper we present an on-line variant of variational Bayesian learning. The method is based on collecting likelihood information as the training samples are processed one at a time and decaying the old likelihood information. The decay or f...

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...

This paper presents a simple framework for Horn­ clause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logical and prob­ abilistic notions of evidential reasoning. This can be used as a basis for a new w...

Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

Naive Bayesian classiiers which make independence assumptions perform remarkably well on some data sets but poorly on others. We explore ways to improve the Bayesian classiier by searching for dependencies among attributes. We propose and evaluate two algorithms for detecting dependencies among attributes and show that the backward sequential elimination and joining algorithm provides the most ...

Driving assistance systems that adapt to an individual driver are essential for avoiding traffic accidents because there are individual differences in the way of driving. To realize such systems, it is necessary to take account of not only observable physical quantities, but also information inferred from observation. For example, a collision avoidance system warns a driver according to an esti...

In the last few years, there has been active research on aggregating advanced statistical measures in multi-dimensional data cubes [10] from partitioned subsets of data. In this paper, we propose a compression and aggregation scheme to support Bayesian estimations in data cubes based on the asymptotic properties of Bayesian statistics. The main application of the technique developed in this pap...

A non-parametric hierarchical Bayesian framework is developed for designing a classifier, based on a mixture of simple (linear) classifiers. Each simple classifier is termed a local "expert", and the number of experts and their construction are manifested via a Dirichlet process formulation. The simple form of the "experts" allows analytical handling of incomplete data. The model is extended to...