A Homotopy Strategy for Accelerated Sampling

Graduate Thesis Or Dissertation

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Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/h415pg78t

Descriptions

Attribute Name	Values
Creator	Jensen, Andrew D.
Abstract	Markov Chain Monte Carlo methods may be used to determine normalizations and moments of distributions. However, these methods may perform poorly when starting from distributions that have little overlap with the target. We develop a homotopy based iterative process of incremental importance sampling to normalize distributions when observations can only be made from those distributions that differ from target. In addition to this topic, discussions are included of my experience in the NSF Risk and Uncertainty Traineeship Program (NRT) applying mathematical knowledge to a transdisciplinary study of the Dungeness crab.
License	All rights reserved
Resource Type	Masters Thesis
Date Issued	2018-08-24
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Mathematics
Degree Grantor	Oregon State University
Commencement Year	2019
Advisor	Restrepo, Juan
Committee Member	Koslicki, David
Academic Affiliation	Mathematics
Urheberrechts-Erklärung	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]

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