Bounds and Low-Rank Approximation for Controlled Markov Processes
Stochastic processes have captivated scientific interest by balancing conceptual simplicity with the ability to model complex, poorly understood, or even entirely unknown phenomena. Still, the deployment of stochastic process models remains challenging in practice due to their intrinsically uncertai...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/155334 https://orcid.org/0000-0002-3704-0191 |