Bayesian statistical practice makes extensive use of versions of objective Bayesian analysis. We discuss why this is so, and address some of the criticisms that have been raised concerning objective Bayesian analysis. The dangers of treating the issue too casually are also considered. In particular, we suggest that the statistical community should accept formal objective Bayesian techniques wit...

the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to enginee...

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 most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...

Summary The performance of different filtering algorithms combined with 3D mapping-aided (3DMA) techniques is investigated in this paper. Several single- and multi-epoch were implemented then tested on static pedestrian navigation data collected the City London using a u-blox EVK M8T GNSS receiver vehicle Canary Wharf, London, by trial van Racelogic Labsat 3 front-end. results show tha...