Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science

Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science

Bibliographic Details
Main Author: Lang, Lixin
Language:English
Published: The Ohio State University / OhioLINK 2008
Subjects:
Statistics
Sequential Monte Carlo
SMC
MCMC
Smoothing
Prior Checking
Predictive Density Value
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu12045822892021-08-03T05:53:19Z Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science Lang, Lixin Statistics Sequential Monte Carlo SMC MCMC Smoothing Prior Checking Predictive Density Value <p>The research effort in this dissertation is targeted to investigate theoretical properties of some key statistics used in the sequential Monte Carlo (SMC) sampling, and to extend SMC to model checking, prior smoothing, and constrained state estimation. A novel application of SMC estimation to population pharmacokinetic models is also introduced.</p><p>Asymptotic properties of two key statistics in the SMC sampling, importance weights and empirical effective samples size, are discussed in the dissertation. The sum-normalized nature of importance weights makes it extremely difficult, if not impossible, to analytically investigate their properties. By using expectation-normalized importance weights, we are able to show the theoretical estimate of empirical effective sample size under various situations. In addition, the superiority of optimal importance function over prior importance function is verified based on the expectation-normalized weights.</p><p>The usage of SMC is also demonstrated for checking incompatibility between the prior and the data, using observation's predictive density value. When the prior is detected to be incompatible with the data, prior smoothing is proposed with a popular numerical method, Moving Horizon Estimation (MHE), to obtain a better estimate of the initial state value. Specifically, the incorporation of MHE smoothing into SMC estimation is among the first efforts to integrate these two powerful tools.</p><p>Convergence of constrained SMC (Chen, 2004) is verified and its performance is further illustrated with a more complex model.</p><p>SMC estimation is applied to a multi-dimensional population pharmacokinetic (PK) model. It is shown that the SMC sampling is faster than Markov Chain Monte Carlo (MCMC), and it doesn't suffer from the lack of convergence concern for MCMC.</p> 2008-03-19 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289 http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Statistics
Sequential Monte Carlo
SMC
MCMC
Smoothing
Prior Checking
Predictive Density Value
spellingShingle Statistics
Sequential Monte Carlo
SMC
MCMC
Smoothing
Prior Checking
Predictive Density Value
Lang, Lixin
Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science
author Lang, Lixin
author_facet Lang, Lixin
author_sort Lang, Lixin
title Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science
title_short Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science
title_full Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science
title_fullStr Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science
title_full_unstemmed Advancing Sequential Monte Carlo For Model Checking, Prior Smoothing And Applications In Engineering And Science
title_sort advancing sequential monte carlo for model checking, prior smoothing and applications in engineering and science
publisher The Ohio State University / OhioLINK
publishDate 2008
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1204582289
work_keys_str_mv AT langlixin advancingsequentialmontecarloformodelcheckingpriorsmoothingandapplicationsinengineeringandscience
_version_ 1719427213688832000

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