The use of bootstrap in computer-intensive Bayesian methods

P. M. Djurić

doi:10.1109/ACSSC.1999.832286

The use of bootstrap in computer-intensive Bayesian methods

P. M. Djurić

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The use of computer-intensive methods in signal processing becomes more frequent as the power of computers continues to increase. Two classes of such methods are Bayesian Monte Carlo sampling and the bootstrap. In general, these types of methods are used in different settings. The Bayesian methods are usually applied in situations where parametric assumptions are made about the densities that generate the observed data, and the bootstrap, in cases where such assumptions are absent. We explore the possibility of combining these methods. The role of the bootstrap is to provide samples for constructing density functions needed for drawing samples which would allow for more accurate integration or optimization carried out by the Bayesian methods.

Original language	English
Title of host publication	Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3-7
Number of pages	5
ISBN (Electronic)	0780357000, 9780780357006
DOIs	https://doi.org/10.1109/ACSSC.1999.832286
State	Published - 1999
Event	33rd Asilomar Conference on Signals, Systems, and Computers, ACSSC 1999 - Pacific Grove, United States Duration: Oct 24 1999 → Oct 27 1999

Publication series

Name	Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers
Volume	1

Conference

Conference	33rd Asilomar Conference on Signals, Systems, and Computers, ACSSC 1999
Country/Territory	United States
City	Pacific Grove
Period	10/24/99 → 10/27/99

Access to Document

10.1109/ACSSC.1999.832286

Cite this

Djurić, P. M. (1999). The use of bootstrap in computer-intensive Bayesian methods. In M. B. Matthews (Ed.), Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers (pp. 3-7). Article 832286 (Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.1999.832286

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title = "The use of bootstrap in computer-intensive Bayesian methods",

abstract = "The use of computer-intensive methods in signal processing becomes more frequent as the power of computers continues to increase. Two classes of such methods are Bayesian Monte Carlo sampling and the bootstrap. In general, these types of methods are used in different settings. The Bayesian methods are usually applied in situations where parametric assumptions are made about the densities that generate the observed data, and the bootstrap, in cases where such assumptions are absent. We explore the possibility of combining these methods. The role of the bootstrap is to provide samples for constructing density functions needed for drawing samples which would allow for more accurate integration or optimization carried out by the Bayesian methods.",

author = "Djuri{\'c}, \{P. M.\}",

note = "Publisher Copyright: {\textcopyright} 1999 IEEE.; 33rd Asilomar Conference on Signals, Systems, and Computers, ACSSC 1999 ; Conference date: 24-10-1999 Through 27-10-1999",

year = "1999",

doi = "10.1109/ACSSC.1999.832286",

language = "English",

series = "Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3--7",

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booktitle = "Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers",

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Djurić, PM 1999, The use of bootstrap in computer-intensive Bayesian methods. in MB Matthews (ed.), Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers., 832286, Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers, vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 3-7, 33rd Asilomar Conference on Signals, Systems, and Computers, ACSSC 1999, Pacific Grove, United States, 10/24/99. https://doi.org/10.1109/ACSSC.1999.832286

The use of bootstrap in computer-intensive Bayesian methods. / Djurić, P. M.
Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 1999. p. 3-7 832286 (Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The use of computer-intensive methods in signal processing becomes more frequent as the power of computers continues to increase. Two classes of such methods are Bayesian Monte Carlo sampling and the bootstrap. In general, these types of methods are used in different settings. The Bayesian methods are usually applied in situations where parametric assumptions are made about the densities that generate the observed data, and the bootstrap, in cases where such assumptions are absent. We explore the possibility of combining these methods. The role of the bootstrap is to provide samples for constructing density functions needed for drawing samples which would allow for more accurate integration or optimization carried out by the Bayesian methods.

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Djurić PM. The use of bootstrap in computer-intensive Bayesian methods. In Matthews MB, editor, Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers. Institute of Electrical and Electronics Engineers Inc. 1999. p. 3-7. 832286. (Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers). doi: 10.1109/ACSSC.1999.832286

The use of bootstrap in computer-intensive Bayesian methods

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