Abstract not found

The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas

ABSTRACT: Affect is considered by most contempo-rary theories to be postcognitive, that is, to occur only after considerable cognitive operations have been ac-complished. Yet a number of experimental results on preferences, attitudes, impression formation, and de-_ cision making, as well as some clinical phenomena, suggest that affective judgments may be fairly inde-pendent of, and precede in time, the sorts of percep-tual and cognitive operations commonly assumed to be the basis of these affective judgments. Affective re-actions to stimuli are often the very first reactions of the organism, and for lower organisms they are the dominant reactions. Affective reactions can occur without extensive perceptual and cognitive encoding, are made with greater confidence than cognitive judg-

and subsequently whmh data sets an active user may read or wrote. Flow controls regulate the dissemination of values among the data sets accessible to a user. Inference controls protect statistical databases by preventing questioners from deducing confidential information by posing carefully designed sequences

■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery

activities of everyday life. In particular, we hypothesize that the dispersion of activities away from households and families increases the opportunity for crime and thus generates higher crime rates. A variety of data is presented in support of the hypothesis, which helps explain crime rate trends

all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.

inference scheme in a more general setting? We compare the marginals computed using loopy propagation to the exact ones in four Bayesian network architectures, including two real-world networks: ALARM and QMR. We find that the loopy beliefs often converge and when they do, they give a good

We introduce a system for sensing complex social systems with data collected from one hundred mobile phones over the course of six months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social

This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction