such as program crashes, assertion violations, and non-termination. Preliminary experiments to unit test several examples of C programs are very encouraging.

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

-mance. This finding cannot be explained by sampling error, feedback sign, or existing theories. The authors proposed a preliminary FI theory (FIT) and tested it with moderator analyses. The central assumption of FIT is that FIs change the locus of attention among 3 general and hierarchically organized levels

Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We

Allocation in stratified sampling based on preliminary tests of significance

marginals at the last two iterations. We only plot the diseases which had non-negligible posterior probability. Loopy Belief Propagation . s---=-o� . a-----' range of prior To test this hypothesis, we reparameterized the pyra mid network as follows: we set the prior probability of the "1"

Design and preliminary testing of an

We examine preliminary testing estimators for re-gression coefficients estimated with data from a com-plex survey. The ordinary least squares estimator is a common choice of researchers, but under an in-formative design, the ordinary least squares estima-tor is biased. The probability weighted

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During the first ELDAS progress meeting we agreed upon the following course of action towards