This paper develops axiomatic foundations for a probabilistic theory of causal strength as difference-making. I proceed in three steps: First, I motivate the choice of causal Bayes nets as an adequate framework for defining and comparing measures of causal strength. Second, I prove several representation theorems for probabilistic measures of causal strength— that is, I demonstrate how these me...

We introduce a Bayesian approach to multiple testing. The method is an extension of the false discovery rate (FDR) method due to Benjamini and Hochberg (1995). We also examine the empirical Bayes approach to simultaneous inference proposed by Efron, Tibshirani, Storey and Tusher (2001). We show that, in contrast to the single hypothesis case – where Bayes and frequentist tests do not agree even...

This paper addresses the laborious task of specifying parameters within a given model of student learning. For example, should the model treat the probability of forgetting a skill as a theory-determined constant? As a single empirical parameter to fit to data? As a separate parameter for each student, or for each skill? We propose a generic framework to represent and mechanize this decision pr...

We propose a new approach for learning Bayesian classifiers from data. Although it relies on traditional Bayesian network (BN) learning algorithms, the effectiveness of our approach lies in its ability to organize and structure the data in such a way that allows us to represent the domain knowledge more accurately than possible in traditional BNs. We use clustering to partition the data into me...

This paper describes a dynamic Bayes net (DBN) language model which allows recognition decisions to be conditioned on features of entities in some environment, to which hypothesized directives might refer. The accuracy of this model is then evaluated on spoken directives in various domains.

There is a critical need for reliably and rapidly detecting, identifying, and tracking submerged low observable targets in port environments, which would allow for rapid and effective neutralization of such threats. Without this capability, personnel, naval platforms and targets of opportunity are exposed to a cheap kill by an opportunistic threat. The goal of this effort is to exploit for the ...

The aim of this study is to rank some features that characterize the psychological dynamics of cooperative team work in order to determine priorities for interventions and formation: leading positive feedback, cooperative manager and collaborative manager features. From a dataset of 20 cooperative sport teams (403 soccer players), the characteristics of the prototypical sports teams are studied...

The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid or diagnosis, in particular thanks to its inference capabilities, even when data are incomplete. For classification tasks, Naive Bayes and Augmented Naive Bayes classifiers have shown excellent performances. Learning a Naive Bayes classifier from incomplete datasets is not difficult as only parame...

Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets (though for different parameterizations), and a third through structural learning. This paper focuses on people's short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-wor...

In this paper, we demonstrate how semantic categories of images can be learnt from their color distributions using an effective probabilistic approach. Many previous probabilistic approaches are based on the Naïve Bayes that assume independence among attributes, which are represented by a single Gaussian distribution. We use a derivative of the Naïve Bayesian classifier, called Flexible Bayesia...