2 options
Advances in the theory of determinantal point processes.
Connect to full text Available online
View online- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Rising, Justin Kyle.
- Language:
- English
- Subjects (All):
- Statistics.
- Mathematics.
- Applied mathematics.
- 0364.
- 0405.
- 0463.
- Penn dissertations--Statistics.
- Statistics--Penn dissertations.
- Local Subjects:
- Penn dissertations--Statistics.
- Statistics--Penn dissertations.
- 0364.
- 0405.
- 0463.
- Physical Description:
- 77 pages
- Contained In:
- Dissertation Abstracts International 75-01B(E).
- System Details:
- Mode of access: World Wide Web.
- text file
- Summary:
- The theory of determinantal point processes has its roots in work in mathematical physics in the 1960s, but it is only in recent years that it has been developed beyond several specific examples. While there is a rich probabilistic theory, there are still many open questions in this area, and its applications to statistics and machine learning are still largely unexplored. Our contributions are threefold. First, we develop the theory of determinantal point processes on a finite set. While there is a small body of literature on this topic, we offer a new perspective that allows us to unify and extend previous results. Second, we investigate several new kernels. We describe these processes explicitly, and investigate the new discrete distribution which arises from our computations. Finally, we show how the parameters of a determinantal point process over a finite ground set with a symmetric kernel may be computed if infinite samples are available. This algorithm is a vital step towards the use of determinantal point processes as a general statistical model.
- Notes:
- Thesis (Ph.D. in Statistics) -- University of Pennsylvania, 2013.
- Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
- Adviser: Lawrence D. Brown.
- Local Notes:
- School code: 0175.
- ISBN:
- 9781303367427
- Access Restriction:
- Restricted for use by site license.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.