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You searched for +publisher:"Penn State University" +contributor:("Dr. Bharath Sriperumbudur, Thesis Advisor/Co-Advisor"). One record found.

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Penn State University

1. Rao, Aniruddha Rajendra. COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES.

Degree: 2019, Penn State University

In this thesis, we consider the problem of estimating an unknown density, p_o belonging to an infinite dimensional exponential family P parametrized by functions in a reproducing kernel Hilbert space (RKHS) H. P is quite rich in the sense that a broad class of densities on R^d can be approximated arbitrarily well in Kullback-Leibler (KL) divergence by elements in it. The main focus of the thesis is to propose and compare the performance of various estimators of p_o. General methods like maximum likelihood estimation (MLE) or pseudo MLE do not result in practically useful estimators due to their inability to efficiently handle the log-partition function. In this work, we consider three different estimators, (i) Kernel Density Estimator (KDE), which is a classical non-parametric density estimator, (ii) Score Matching Estimator (SME), based on minimizing the Fisher divergence, J(p_o||p) between p_o and p in P, which involves solving a simple finite-dimensional linear system and (iii) Approximate Matching estimator (AME), which is a variation of SME but computationally more efficient. We show through numerical simulations that KDE performs better in the univariate case, while the other two methods have superior performance in high dimensional scenarios. Advisors/Committee Members: Dr. Bharath Sriperumbudur, Thesis Advisor/Co-Advisor, Dr. Ephraim Hanks, Committee Member, Benjamin Shaby, Committee Member.

Subjects/Keywords: Kernel Methods; Density Estimation; Exponential Family; Computation; Infinite Dimension

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rao, A. R. (2019). COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16246arr30

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rao, Aniruddha Rajendra. “COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES.” 2019. Thesis, Penn State University. Accessed October 31, 2020. https://submit-etda.libraries.psu.edu/catalog/16246arr30.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rao, Aniruddha Rajendra. “COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES.” 2019. Web. 31 Oct 2020.

Vancouver:

Rao AR. COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES. [Internet] [Thesis]. Penn State University; 2019. [cited 2020 Oct 31]. Available from: https://submit-etda.libraries.psu.edu/catalog/16246arr30.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rao AR. COMPARISON OF DIFFERENT DENSITY ESTIMATORS FOR INFINITE DIMENSIONAL EXPONENTIAL FAMILIES. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16246arr30

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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