We explore past and recent developments in rare-event probability estimation with a particular focus on a novel Monte Carlo technique – Empirical Likelihood Maximization (ELM). This is a versatile method that involves sampling from a sequence of densities using MCMC and maximizing an empirical likelihood. The quantity of interest, the probability of a given rare-event, is estimated by solving a...

This paper presents a novel segmentation approach featuring shape constraints of multiple structures. A framework is developed combining statistical shape modeling with a maximum a posteriori segmentation problem. The shape is characterized by signed distance maps and its modes of variations are generated through principle component analysis. To solve the maximum a posteriori segmentation probl...

In this work we propose a clustering algorithm that learns on-line a finite gaussian mixture model from multivariate data based on the expectation maximization approach. The convergence of the right number of components as well as their means and covariances is achieved without requiring any careful initialization. Our methodology starts from a single mixture component covering the whole data s...

purpose: the present paper aims to relate self-efficacy as an important psychological construct to information literacy as an essential requisite of the information society.methodology: reviewing related literature in communication, sociology, information science and computer science.findings: self-efficacy could increase interest, efforts, enjoy, self-regulation and performance. it is consider...

The logic of backward induction (BI) in perfect information (PI) games has been intensely scrutinized for the past quarter century. A major development came in 2002, when P. Battigalli and M. Sinischalchi (BS) showed that an outcome of a PI game is consistent with common strong belief of utility maximization if and only if it is the BI outcome. Both BS’s formulation, and their proof, are comple...

The goal of this study was to examine age effects on the ability/willingness to wait for large rewards in a real temporal reward discounting task from childhood to adulthood. Therefore, a real temporal discounting (TD) task was administered to children aged 6-12 (n = 39), adolescents aged 13-17 (n = 28), and young adults aged 18-19 (n = 55). Findings indicated that the cross-sectional developme...

Network lifetime maximization is a critical problem for longterm data collection in wireless sensor networks. For large-scale networks, distributed and self-adaptive solutions are highly desired. In this paper, we investigate how to optimize the network lifetime by a localized method. Specifically, the network lifetime maximization problem is converted to a localized cost-balancing problem with...

In order to measure fuzzy event, credibility measure is proposed as a non-additive set function. In this paper, this concept is extended to fuzzy sets. First, a mean measure is defined by Lebesgue integral and some properties are investigated such as the monotonicity theorem, self-duality theorem and subadditivity theorem. Furthermore, by using Sugeno integral, an equilibrium measure is propose...

Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universa...

Maximization refers to a decision-making style that involves seeking the single best option when making a choice, which is generally dysfunctional because people are limited in their ability to rationally evaluate all options and identify the single best outcome. The vocational consequences of maximization are examined in two samples, college students and working adults. After controlling for t...