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Bayesian Monte Carlo signal processi...

Guo, Dong.

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Bayesian Monte Carlo signal processing and its applications in communications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Monte Carlo signal processing and its applications in communications./
Author:	Guo, Dong.
Description:	276 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2011.
Contained By:	Dissertation Abstracts International65-04B.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3129057
ISBN:	0496763175

Bayesian Monte Carlo signal processing and its applications in communications.
Guo, Dong.

Bayesian Monte Carlo signal processing and its applications in communications. - 276 p.

Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2011.

Thesis (Ph.D.)--Columbia University, 2004.

In this thesis we are concerned with the nonlinear filtering problems associated with many highly-complex dynamic systems using Bayesian Monte Carlo methods. We study some issues related to the Bayesian Monte Carlo methods and their performance

ISBN: 0496763175Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Bayesian Monte Carlo signal processing and its applications in communications.
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245 1 0 $a Bayesian Monte Carlo signal processing and its applications in communications.
300 $a 276 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2011.
500 $a Adviser: Xiaodong Wang.
502 $a Thesis (Ph.D.)--Columbia University, 2004.
520 $a In this thesis we are concerned with the nonlinear filtering problems associated with many highly-complex dynamic systems using Bayesian Monte Carlo methods. We study some issues related to the Bayesian Monte Carlo methods and their performance
520 $a The first issue that is addressed is the improvements for SMC methods. Employing the wavelet decomposition on the random process which cannot be modelled with a linear dynamic model (i.e., ARMA model), we extend the SMC method to deal with more
520 $a Dynamic systems usually exhibit strong memory effects, i.e., future observations can reveal substantial information about the current state. In order to take the advantage of the properties, we develop two novel sampling scheme for delayed estim
520 $a We also study the multilevel mixture Kalman filter to reduce the complexity of the mixture Kalman filter. Multilevel mixture Kalman filter mainly makes use of the multilevel or hierarchical structure of the space from which the indicator variabl
520 $a The efficiency of SMC methods are closely related with the proposal distribution. Based on this point, we develop several efficient Kernel-based sequential Monte Carlo methods. In the methods, we represent the discrete samples with Gaussian Kern
520 $a Finally, a general framework for quasi-Monte Carlo particle filter is proposed. Novel extensions to the proposed quasi-Monte Carlo particle filter are also provided based on the deterministic filtering and adaptive importance sampling scheme. (A
590 $a School code: 0054.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Statistics. $3 517247
690 $a 0544
690 $a 0463
710 2 0 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 65-04B.
790 1 0 $a Wang, Xiaodong, $e advisor
790 $a 0054
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3129057