Thesiss

This dissertation offers computational and theoretical advances for optimization under uncertainty problems that utilize a probabilistic framework for addressing such uncertainties, and adopt a probabilistic performance as objective function. Emphasis is placed on applications t…

First chapter of my dissertation uses an EGARCH method and a Stochastic Volatility (SV) method which relies upon Markov Chain Monte Carlo (MCMC) framework based on Efficient Importance Sampling (EIS) to model inflation volatility of Turkey. The strength of SV model lies in its s…

For many problems, especially nonlinear systems, the reliability assessment must be done in the time domain. Monte-Carlo simulation (MCS) can accurately assess the reliability of the system. However, its computational cost is highly expensive for the complex dynamic system. Impo…