Bayesian model comparison provides a rational and consistent method for applying logic and probability to the problem of evaluating models. Model comparison requires numerical techniques that are usually very time consuming to run. This dissertation proposes extensions to several existing numerical model comparison techniques, including nested sampling and thermodynamic integration, that incorporate parallel algorithm design to achieve significant speed-ups. Serial computer performance gains have slowed in recent years, and most processing speed improvements are seen in the area of parallel architectures. This work discusses the design, theoretical analysis, and empirical analysis of these algorithms, focusing on the performance of these algorithms with respect to accuracy and run time. Many disciplines in science and engineering make use of existing model comparison techniques. This work aims to save investigators in these disciplines time, and potentially attract those who may have been put off by time complexity concerns, by developing a general approach to model comparison that takes full advantage of modern parallel computing platforms.
|Advisor:||Goggans, Paul M.|
|Commitee:||Daigle, John, Cao, Lei, Chen, Yixin|
|School:||The University of Mississippi|
|School Location:||United States -- Mississippi|
|Source:||DAI-B 81/2(E), Dissertation Abstracts International|
|Subjects:||Electrical engineering, Statistics, Computer science|
|Keywords:||Bayesian inference, MCMC, Model comparison, Nested sampling, Thermodynamic integration|
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