Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highes...

Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the n...

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 ...