Evolving Coauthorship Modeling and Prediction via Time-Aware Paired Choice Analysis

Time Series Prediction via Density Ratio Modeling

Modeling and prediction of time-series of monthly copper prices

One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...

Formal Modeling and Analysis of Power-Aware Real-Time Systems

The paper describes a unified formal framework for designing and reasoning about power-constrained, timed systems. The framework is based on process algebra, a formalism which has been developed to describe and analyze communicating, concurrent systems. The proposed extension allows the modeling of probilistic resource failures and power consumption by resources within the same formalism. Thus,...

Modeling Paired Ordinal Response Data

 About 25 years ago, McCullagh proposed a method for modeling univariate ordinal responses. After publishing this paper, other statisticians gradually extended his method, such that we are now able to use more complicated but efficient methods to analyze correlated multivariate ordinal data, and model the relationship between these responses and host of covariates. In this paper, we aim to...

Modeling Mate-Choice using Computational Temperature and Dynamically Evolving Representations

We present a model of mate-choice (KAMA) that is based on a gradual, stochastic process of representation-building leading to marriage. KAMA reproduces empirically verifiable population-level mate-selection behavior using individual-level mate-choice mechanisms. Individuals have character profiles, which describe a number of their characteristics (physical beauty, potential earning power, etc.)...