Bayes’ inference cannot reliably account for uncertainty in mental processes. The reason is that based on the assumption order which relevant features are evaluated indifferent, not case most of Instead rule, a more general, probabilistic rule capable accounting these effects established. This new can be used to improve current Bayesian models cognition. Moreover, it should play an essential ro...

It is well known that a social choice function is truthfully implementable in Bayesian Nash equilibrium if and only if it is incentive compatible. However, in general it is not possible to rule out other equilibrium outcomes, and additional conditions, e.g., Bayesian monotonicity, are needed to ensure full implementation. We argue that this multiplicity problem is not very severe for virtual Ba...

We consider the mechanism design problem when agents’ types are multidimensional and continuous, and their valuations are interdependent. If there are at least three agents whose types satisfy a weak correlation condition, then for any decision rule and any ε > 0 there exist balanced transfers that render truthful revelation a Bayesian ε-equilibrium. A slightly stronger correlation condition en...

Algorithms for learning cIassification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This iutroduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to QuinIan’s information gain, while smoothing and averaging replace p...

Abst rac t . Object recognition is the classification of objects into one of many a p m o r i known object classes. In addition, it may involve the estimation of the pose of tile object and/or the track of the object in a sequence of images. Bayesian statistical pattern recognition, neural networks and rule based syst~ems have been used to address the object recognition problem. In the case of ...

Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extend...

In a Bayesian approach to online learning a simple approximate parametric form for posterior is updated in each online learning step. Usually in online learning only an estimate of the solution is updated. The Bayesian online approach is applied to two simple learning scenarios, learning a perceptron rule with respectively a spherical and a binary weight prior. In the rst case we rederive the r...

In this paper ongoing work on an approach for planning sensing actions and controlling intelligent, purposive robotic systems is presented. The method uses Bayesian Decision Analysis (BDA) for deciding what sensing actions should be performed. This ooers a probabilistic framework that provides a more dynamic and modular behaviour than traditional rule based planners. Experiments show that the B...

Bayesian wavelet shrinkage methods are defined through a prior distribution on the space of wavelet coefficients after a Discrete Wavelet Transformation has been applied to the data. Posterior summaries of the wavelet coefficients establish a Bayes shrinkage rule. After the Bayes shrinkage is performed, an Inverse Discrete Wavelet Transformation can be used to recover the signal that generated ...

Kalai and Lehrer (93a, b) have shown that if players' beliefs about the future evolution of play is absolutely continuous with respect to play induced by optimal strategies then Bayesian updating eventually leads to Nash equilibrium. In this paper, we present the ̄rst set of necessary and su±cient conditions that ensure that Bayesian updating eventually leads to Nash equilibrium. More important...