MSc. Thesis Defense:Alara Güler
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Alara Güler
Industrial Engineering, MSc. Thesis, 2018


Thesis Jury

Assist. Prof. Dr. Sinan Yıldırım(Thesis Advisor), Prof. Dr. Ş. İlker Birbil (Thesis Co-Advisor), Assist. Prof. Dr. Kamer Kaya, Assoc. Prof. Dr. Ali Taylan Cemgil, Prof. Dr. Berrin Yanıkoğlu



Date & Time: January 10th, 2018 –  10 AM

Place: FENS L063

Keywords : Parameter estimation, Markov chain Monte Carlo methods, Origin-Destination Matrix problem, Metropolis-Hastings within Gibbs algorithm, Bayesian inference, Parameter estimation with Differential Privacy





This thesis presents a study of estimating the probability matrix of an origin-destination model associated with a two-way transportation line with the help of Bayesian inference and Markov chain Monte Carlo methods, more specifically, Metropolis within Gibbs algorithm. Collecting exact count data of a transportation system is often not possible due to technical insufficiencies or data privacy issues. This thesis concentrates on utilization of Markov chain Monte Carlo Methods for two origin-destination problems: one that assumes missing departure data and one that assumes the availability of differentially private data instead of the complete data. Different models are formulated for those two data conditions that are under study. The experiments are conducted with synthetically generated data and the performance of each model under these conditions were measured. It has been concluded that MCMC methods can be useful for effectively estimating the probability matrix of certain OD problems.