<p>This dissertation focuses on the simulation efficiency of the Markov process for two scenarios: Stochastic differential equations(SDEs) and simulated weather data. </p>
<p><br></p>
<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>
<p><br></p>
<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/21601611 |
| Date | 05 December 2022 |
| Creators | Qi Wang (14157183) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Exact_Markov_Chain_Monte_Carlo_for_a_Class_of_Diffusions/21601611 |
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