Human ability to simultaneously track multiple items declines with set size. This effect is commonly attributed to a fixed limit on the number of items that can be attended to, a notion that is formalized in limited-capacity and slot models. Instead, we propose that observers are constrained by stimulus uncertainty that increases with the number of items but use Bayesian inference to achieve op...

Our objective is to study the contribution of naive increased Bayesian networks in problems of image classification. The images used in this study represent the structure of a document containing text blocks and graphics. We proposed three variants of Bayesian networks. First naive Bayesian networks RN who, despite their simple structure and strong assumption on independence have given very goo...

Integrating the expressive power of first-order logic with the ability of probabilistic reasoning of Bayesian networks has attracted the interest of many researchers for decades. We present an approach to integration that translates logical knowledge into Bayesian networks and uses Bayesian network composition to build a uniform representation that supports both logical and probabilistic reason...

Bayesian statistics is usually viewed as an 'advanced' course; Bayesian methods are either seen as an optional extension or its founding ideas are seen as difficult to grasp. There are, however, two good reasons for changing this. First, the recent strong increase in interest in Bayesian methods, in several areas. Second, the fact that Bayesian methods are particularly relevant for the kinds of...

Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to Support Vector machines. Based on simple probabilistic models, they render interpretable results and can be embedded in Bayesian frameworks for model selection, feature selection, etc. In this paper, by applying the PAC-Bayesian theorem of nc...

The application of Bayesian approach in clinical trials becomes more useful over classical method. It is beneficial from design to analysis phase. The straight forward statement is possible to obtain through Bayesian about the drug treatment effect. Complex computational problems are simple to handle with Bayesian techniques. The technique is only feasible to performing presence of prior inform...

Abstract. The Pathfinder model was developed to fill a perceived gap within the range of existing simple climate models. is compilation formulations describing and carbon cycle systems, chosen for their balance between mathematical simplicity physical accuracy. resulting enough be used with Bayesian inference algorithms calibration, which enables assimilation latest data from complex Earth syst...

We describe the results of performing data mining on a challenging medical diagnosis domain, acute abdominal pain. This domain is well known to be difficult, yielding little more than 60% predictive accuracy for most human and machine diagnosticians. Moreover, many researchers argue that one of the simplest approaches, the naive Bayesian classifier, is optimal. By comparing the performance of t...

Naive Bayes is a simple Bayesian network classifier with strong independence assumptions among features. This classifier despite its strong independence assumptions, often performs well in practice. It is believed that relaxing the independence assumptions of naive Bayes may improve the performance of the resulting structure. Augmented Bayesian Classifiers relax the independence assumptions of ...