report the true subjective probability of a binary event. Alternatively, one must carefully calibrate inferences about true subjective probabilities from elicited subjective probabilities over binary events, recognizing the incentives that risk averse agents have to report the same probability

suggestions from multiple advisors. In this paper, we study the quality of aggregation of multiple subjective probability distributions of future temperatures, using data collected by Abbas, Budescu, Yu and Haggerty (2008), as a function of 4 factors – the elicitation method (Fixed Probability versus Fixed

We use data from the Survey of Professional Forecasters (SPF) to compare point predictions of gross domestic product (GDP) growth and inflation with the subjective probability distributions held by fore-casters. We find that most SPF point predictions are quite close to the central tendencies

This research presents entrepreneuring as a puzzle where entrepreneurs venture at a risk-return level that is worse than that of the private equity index and much worse than the public equity index. Under subjective rationality, we explain this puzzle by the possibility that entrepreneurs confi

, etc However, each player has a subjective probability distribution over the alternative possibibties In most of the paper it is assumed that these probability distributions entertained by the different players are mutually "consistent", in the sense that they can be regarded as conditional

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A method is presented to approximate optimally an n-dimensional discrete probability distribution by a product of second-order distributions, or the distribution of the first-order tree dependence. The problem is to find an optimum set of n-1 first order dependence relationship among the n

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

Distributed Sensor Networks (DSNs) are ad-hoc mobile networks that include sensor nodes with limited computation and communication capabilities. DSNs are dynamic in the sense that they allow addition and deletion of sensor nodes after deployment to grow the network or replace failing and unreliable

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur