Bayesian network models are widely used for supervised prediction tasks such as classification. Usually the parameters of such models are determined using ‘unsupervised’ methods such as likelihood maximization, as it has not been clear how to find the parameters maximizing the supervised likelihood or posterior globally. In this paper we show how this supervised learning problem can be solved e...

We consider the problem of learning the parameters of a Bayesian network from data, while taking into account prior knowledge about the signs of influences between variables. Such prior knowledge can be readily obtained from domain experts. We show that this problem of parameter learning is a special case of isotonic regression and provide a simple algorithm for computing isotonic estimates. Ou...

Probabilistic Neural Networks (PNNs) constitute a promisingmethodology for classification and prediction tasks. Their performance depends heavily on several factors, such as their spread parameters, kernels, and prior probabilities. Recently, Evolutionary Bayesian PNNs were proposed to address this problem by incorporating Bayesian models for estimation of spread parameters, as well as Particle...

If we think of the graph as modeling a network, the vulnerability measure the resistance of the network to disruption of operation after the failure of certain stations or communication links. Many graph theoretical parameters have been used to describe the vulnerability of communication networks, including connectivity, integrity, toughness, binding number and tenacity.In this paper we discuss...

The origins of life likely required the cooperation among a set of molecular species interacting in a network. If so, then the earliest modes of evolutionary change would have been governed by the manners and mechanisms by which networks change their compositions over time. For molecular events, especially those in a pre-biological setting, these mechanisms have rarely been considered. We are o...

Abstract—Wireless communication systems either ad hoc or infrastructure mode the key challenges that must be overcome to realize the practical benefits of Quality of Service (QoS). Generally the QoS is the ability for network element to provide some level of assurance for consistent network data delivery. The ability of Wireless Local Area Network (WLAN) to support real-time services is possibl...

This paper proposes a bi-objective reliable supply chain network design that immunizes the network against different sources of uncertainties. In this regard, scenario based stochastic programming method is applied to model different disruption scenarios affecting accurate performance of network stages. Also, reliable and unreliable facilities are suggested for lessening vulnerability of networ...

The aim of this paper is to propose a robust reliable bi-objective supply chain network design (SCND) model that is capable of controlling different kinds of uncertainties, concurrently. In this regard, stochastic bi-level scenario based programming approach which is used to model various scenarios related to strike of disruptions. The well-known method helps to overcome adverse effects of disr...