Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks

Demand Response Based Model for Optimal Decision Making for Distribution Networks

In this paper, a heuristic mathematical model for optimal decision-making of a Distribution Company (DisCo) is proposed that employs demand response (DR) programs in order to participate in a day-ahead market, taking into account elastic and inelastic load models. The proposed model is an extended responsive load modeling that is based on price elasticity and customers’ incentives in which they...

Decision support using Bayesian networks for clinical decision making

This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the thesis demonstrates four novel applications of BN and dynamic discretization to clinical problems. Firstly, it demonstrates the flexibility of the Dynamic Discretization Algorithm in modeling existing medical knowledge using ...

An AHP-Delphi Multi-Criteria Decision Making Model with Application to Environmental Decision-Making

Quantum-Like Bayesian Networks for Modeling Decision Making

In this work, we explore an alternative quantum structure to perform quantum probabilistic inferences to accommodate the paradoxical findings of the Sure Thing Principle. We propose a Quantum-Like Bayesian Network, which consists in replacing classical probabilities by quantum probability amplitudes. However, since this approach suffers from the problem of exponential growth of quantum paramete...

Fusion of Modular Bayesian Networks for Context-Aware Decision Making

Ubiquitous computing brings various information and knowledge derived from different sources, under which Bayesian networks are widely used to cope with the uncertainty and imprecision. In this paper, we propose a modular Bayesian network system to extract context information by cooperative inference of multiple modules, which guarantees reliable inference compared to the monolithic Bayesian ne...