the development of optimization and mathematical models for closed loop supply chain (clsc) design has attracted considerable interest over the past decades. however, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. the aim of this paper, therefore, is to propose a n...

Diffusion of innovations and knowledge is in most cases accounted for by the logistic model. Fieldwork research however constantly report that empirical data utterly deviate from this mathematical function. This chapter scrutinizes network forcing of diffusion process. The departure of empirical data from the logistic function is explained by social network discreteness, heterogeneity and aniso...

This paper proposes the use of Bayesian Causal Maps (BCM s) to analyze the complex structure of inflation in Turkey. In this study, a model of inflation is initially structured using a cognitive mapping technique; the dependent probabilities of the concepts are then calculated based on the detailed analysis of past data. Finally, a BCM is used to analyze the complex structure of inflation in Tu...

A traditional way to design a binary response experiment is to design the experiment to be most efficient for a best guess of the parameter values. A design which is optimal for a best guess however may not be efficient for parameter values close to that best guess. We propose designs which formally account for the prior uncertainty in the parameter values. A design for a situation where the be...

This paper addresses the fundamental problem of document classification, and we focus attention on classification problems where the classes are mutually exclusive. In the course of the paper we advocate an approximate sampling distribution for word counts in documents, and demonstrate the model’s capacity to outperform both the simple multinomial and more recently proposed extensions on the cl...

We present a new approach to training back-propagation artificial neural nets (BP-ANN) based on regularization and cross-validation and on initialization by a logistic regression (LR) model. The new approach is expected to produce a BP-ANN predictor at least as good as the LR-based one. We have applied the approach to ten data sets of biomedical interest and systematically compared BP-ANN and L...

Logistic regression is the standard method for developing prognostic models for intensive care, but this approach does not take into account the uncertainty in the model selected and the uncertainty in its regression coefficients. This weakness can be addressed by adopting a Bayesian model-averaged approach to logistic regression; however, with respect to the dataset used for our study, we foun...