Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


25736.pdf (8.05 MB)

ETD Abstract Container

Abstract Header

Nonstationary Nearest Neighbors Gaussian Process Models

Hanandeh, Ahmad Ali
http://orcid.org/0000-0001-9208-7083
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504781089107666

Abstract Details

2017, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
Modeling is an essential part of research and development in almost every sphere of modern life. Computer models are frequently used to explore physical systems, but can be computationally expensive to evaluate (take days, weeks, or possibly months to run single simulation at one input value). In such settings, an emulator is used as a surrogate. Gaussian Process (GP) is a common and very useful way to develop emulators to describe the output of computer experiments and to describe computationally expensive simulations in uncertainty quantification. Recently, much attention has been paid about dealing with large datasets which can be found in various fields of the natural, social sciences and modern instruments. This resulted in an increasing need for methods to analyze large datasets. However, GP is nonparametric, meaning that the complexity of the model grows as more data points are received, and as a result, it faces several computational challenges for modeling large datasets, because of the need of calculating the inverse and determinant of large, dense and unstructured matrix; therefore we need alternative methods to analyze such large datasets. Various methods have been developed to deal with this problem, including a reduced rank approach and a sparse matrix approximation. However, most of them rely on unrealistic assumptions for the underlying process such as stationarity. We develop a new approximation
Bledar Konomi, Ph.D. (Committee Chair)
Emily Kang, Ph.D. (Committee Member)
Hang Joon Kim, Ph.D. (Committee Member)
Siva Sivaganesan, Ph.D. (Committee Member)
105 p.
Statistics
Bayesian hierarchical modeling; Large datasets; Binary tree; TOMS ozone data; Gaussian process; Nonstationary covariance function

Recommended Citations

Citations

  • Hanandeh, A. A. (2017). Nonstationary Nearest Neighbors Gaussian Process Models [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504781089107666

    APA Style (7th edition)

  • Hanandeh, Ahmad. Nonstationary Nearest Neighbors Gaussian Process Models. 2017. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504781089107666.

    MLA Style (8th edition)

  • Hanandeh, Ahmad. "Nonstationary Nearest Neighbors Gaussian Process Models." Doctoral dissertation, University of Cincinnati, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504781089107666

    Chicago Manual of Style (17th edition)

ucin1504781089107666
289
© 2017, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.