The general problem of computing poste rior probabilities in Bayesian networks is NP hard Cooper However e cient algorithms are often possible for particular applications by exploiting problem struc tures It is well understood that the key to the materialization of such a possibil ity is to make use of conditional indepen dence and work with factorizations of joint probabilities rather than joi...

Bayes’ theorem tells us how to optimally predict the class of a previously unseen example, given a training sample. The chosen class should be the one which maximizes P(CilE) = P(Ci) P(EICi) /P(E), where Ci is the ith class, E is the test example, P(YIX) denotes the conditional probability of Y given X, and probabilities are estimated from the training sample. Let an example be a vector of a at...

—Determining whether a species has gone extinct is a central problem in both paleobiology and conservation biology. Past literature has mostly employed equations that yield confidence intervals around the endpoints of temporal ranges. These frequentist methods calculate the chance of not having seen a species lately given that it is still alive (a conditional probability). However, any reasonab...

In political science and related disciplines in the social and behavioral sciences, there exists an unfortunate de facto divide between qualitative and quantitative empirical research. Sometimes this divide is purely a function of training and disciplinary socialization, but often it reflects a valid dispute over the philosophical foundations of inquiry. I argue here that the Bayesian approach ...

We give here a comparison of the expected outcome theory, the expected utility theory, and the Bayesian decision theory, by way of a simple numerical toy problem in which we look at the investment willingness to avert a high impact low probability event. It will be found that for this toy problem the modeled investment willingness under the Bayesian decision theory is minimally three times high...

In this paper we present the Relational Bayesian Classifier (RBC), a modification of the Simple Bayesian Classifier (SBC) for relational data. There exist several Bayesian classifiers that learn predictive models of relational data, but each uses a different estimation technique for modeling heterogeneous sets of attribute values. The effects of data characteristics on estimation have not been ...

The environment that surrounds us is very complex. Understanding and interpreting it is a very hard task. This paper proposes an approach allowing simple form recognition with a camera by using a probabilistic approach called Bayesian Programming. The main goal is to recognize several type of elemental features composing an image, such as local orientation of a contour, or corners. The Bayesian...

Simple Propagation (SP) was recently proposed as a new join tree propagation algorithm for exact inference in discrete Bayesian networks and empirically shown to be faster than Lazy Propagation (LP) when applied on optimal (or close to) join trees built from real-world and benchmark Bayesian networks. This paper extends SP in two directions. First, we propose and empirically evaluate eight heur...

This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure and observes noisy signals of their quality with the goal of confidently identifying the best design after a small number of measurements. I propose three simple Bayesian algorithms for adaptively al...