Browsing by Author "Pati, Debdeep"
Now showing items 1-20 of 25
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Zilber, Daniel S (2021-06-02)Gaussian processes are a powerful and flexible class of nonparametric models that use covariance functions, or kernels, to describe correlations across data. In addition to expressing realistic assumptions, correlation ...
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Chuu, Eric Jason (2022-06-23)We consider the estimation of the marginal likelihood in Bayesian statistics, a essential and important task known to be computationally expensive when the dimension of the parameter space is large. We propose a general ...
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Lee, Se Yoon (2021-04-12)This dissertation explores various applications of Bayesian hierarchical modeling to accommodate general characteristics of the modern data: complex data structure, high-dimensionality, and huge data size. The focus of the ...
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Alexander, Brittany Marie (2022-02-10)Public opinion polling data has unique features that complicate statistical inference. Polling data is often non-representative of the population it aims to estimate. It is also common for individual polls to have a large ...
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Ray, Pallavi (2021-06-29)This dissertation focuses on Bayesian semiparametric regression techniques under constrained setting, and its methodological, computational and applied aspects. We study several non-traditional statistical problems that ...
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Luo, Zhao Tang (2022-04-20)In many applications, spatial data often display heterogeneous dependence patterns and may be subject to irregular geographic constraints. In light of these challenges, this dissertation develops several novel Bayesian ...
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Merchant, Naveed Nabeel (2022-02-28)A new framework for nonparametrically testing of equality of two densities is proposed. From this framework, two different tests are constructed. The two tests themselves are then investigated and compared to other tests ...
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Acharyya, Satwik (2020-07-02)My dissertation focuses on developing Bayesian methodology for complex data structures with an emphasis on building novel algorithms to reduce the computational complexity. One viewpoint of this dissertation is to develop ...
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Zhou, Fangting (2022-07-10)Causal relationship, rather than statistical association, provides the basic understanding of nature. Learning causal structure from observational data is one of the most fundamental ways to explore causality, and the ...
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Amalladinne, Vamsi Krishna (2021-08-02)The broad theme of this dissertation is design of coding schemes that demonstrate good error performance at a low computational cost for the unsourced random access channel. Unsourced random access is a novel multiple ...
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Armandpour, Mohammadreza (2022-04-11)The promise of deep learning is to discover rich, hierarchical models that represent probability distributions over the kinds of data encountered in artificial intelligence applications, such as natural images, audio ...
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Pramanik, Sandipan (2022-06-03)We propose efficient priors for two different statistical problems: (1) designing Bayesian hypothesis tests with reduced costs for detecting the presence or absence of hypothesized effects, and (2) efficient modeling of ...
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Ghosh, Indrajit (2022-06-15)This dissertation focuses on solving some of the most interesting theoretical and methodological questions arising out of various different disciplines with a Bayesian perspective. With the advent of large scale dataset ...
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Zhang, Jingjie (2020-11-18)Gaussian processes (GPs) are widely used in geospatial analysis, machine learning and many application areas. We propose novel scalable methods to tackle two problems in Gaussian process modeling for large spatial datasets. In ...
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Li, Jiangyuan (2023-07-18)Modern machine learning tasks often involve the training of over-parameterized models and the challenge of addressing data bias. However, despite recent advances, there remains a significant knowledge gap in these areas. ...
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Li, Jiangyuan (2023-07-18)Modern machine learning tasks often involve the training of over-parameterized models and the challenge of addressing data bias. However, despite recent advances, there remains a significant knowledge gap in these areas. ...
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Ding, Patrick (2022-07-10)This dissertation contains four projects involving applications of truncated multivariate normal sampling and multivariate normal probability estimation for linearly constrained domains. These two problems have a large ...
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Lapanowski, Alexander Frank (2020-07-21)Large-sample data became prevalent as data acquisition became cheaper and easier. While a large sample size has theoretical advantages for many statistical methods, it presents computational challenges either in the form ...
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Pati, Debdeep; Bhattacharya, Anirban; Cheng, Guang (Journal of Machine Learning Research - JMLR, 2015)
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Pati, Debdeep; Bhattacharya, Anirban; Pillai, Natesh S.; Dunson, David (Annals of Statistics, 2014)