Markov chain Monte Carlo synthetic data generation from casino slot floor event data

dc.contributor.authorBonner, Courtney
dc.contributor.examiningcommitteeLeung, Carson (Computer Science) Arino, Julien (Mathematics)en_US
dc.contributor.supervisorMuthukumarana, Saman (Statistics)en_US
dc.date.accessioned2022-01-24T18:00:16Z
dc.date.available2022-01-24T18:00:16Z
dc.date.copyright2022-01-19
dc.date.issued2022-01en_US
dc.date.submitted2022-01-11T18:01:27Zen_US
dc.date.submitted2022-01-19T21:10:12Zen_US
dc.degree.disciplineStatisticsen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractThe gaming industry produces vast amounts of data that can inform on many different kinds of research problems. Unfortunately, this data is rarely made available for research purposes and when it is, there are often limitations presented by the researcher's need to protect the data source's identity. In an effort to solve this problem, a Markov chain based statistical process for producing synthetic slot floor event data that is realistic and statistically similar to a provided real data set is proposed. Due to the uniqueness of this statistical process, two new methods of transition probability matrix estimation are introduced. This statistical process and the new probability matrix estimation methods are tested on an anonymous data set. The process is able to replicate event data and resultant session data distributions well, but falters in approximating a realistic time series.en_US
dc.description.noteFebruary 2022en_US
dc.identifier.urihttp://hdl.handle.net/1993/36210
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectCasino slot floor, Markov chain, Monte Carlo, MCMC, Synthetic data, Slot machineen_US
dc.titleMarkov chain Monte Carlo synthetic data generation from casino slot floor event dataen_US
dc.typemaster thesisen_US
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