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Exact Markov Chain Monte Carlo for a Class of Diffusions

<p>This dissertation focuses on the simulation efficiency of the Markov process for two scenarios: Stochastic differential equations(SDEs) and simulated weather data. </p>
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<p>For SDEs, we propose a novel Gibbs sampling algorithm that allows sampling from a particular class of SDEs without any discretization error and shows the proposed algorithm improves the sampling efficiency by orders of magnitude against the existing popular algorithms.  </p>
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<p>In the weather data simulation study, we investigate how representative the simulated data are for three popular stochastic weather generators. Our results suggest the need for more than a single realization when generating weather data to obtain suitable representations of climate. </p>

  1. 10.25394/pgs.21601611.v2
Identiferoai:union.ndltd.org:purdue.edu/oai:figshare.com:article/21601611
Date05 December 2022
CreatorsQi Wang (14157183)
Source SetsPurdue University
Detected LanguageEnglish
TypeText, Thesis
RightsCC BY 4.0
Relationhttps://figshare.com/articles/thesis/Exact_Markov_Chain_Monte_Carlo_for_a_Class_of_Diffusions/21601611

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