國立虎尾科技大學 |

On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling./
Author:	Kaplan, Andrea.
Description:	1 online resource (127 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Contained By:	Dissertation Abstracts International79-02B(E).
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355335415

On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling.
Kaplan, Andrea.

On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling. - 1 online resource (127 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Markov chain Monte Carlo (MCMC) is a computational statistical approach for numerically approximating distributional quantities useful for inference that might otherwise be intractable to directly calculate. A challenge with MCMC methods is developing implementations which are both statistically rigorous and computationally scalable to large data sets. This work generally aims to bridge these aspects by exploiting conditional independence, or Markov structures, in data models. Chapter 2 investigates the model properties and Bayesian fitting of a graph model with Markovian dependence used in deep machine learning and image classification, called a restricted Bolzmann machine (RBM), and Chapter 3 presents a framework for describing inherent instability in a general class of models which includes RBMs. Chapters 4 and 5 introduce a fast method for simulating data from a Markov Random Field (MRF) by exploiting conditional independence specified in the model and a flexible R package that implements the approach in C++.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355335415Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling.
LDR:02364ntm a2200337Ki 4500 001 911269
005 20180529081901.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355335415
035 $a (MiAaPQ)AAI10605132
035 $a (MiAaPQ)iastate:16681
035 $a AAI10605132
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Kaplan, Andrea. $3 1182974
245 1 0 $a On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling.
264 0 $c 2017
300 $a 1 online resource (127 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
500 $a Advisers: Daniel J. Nordman; Stephen B. Vardeman.
502 $a Thesis (Ph.D.) $c Iowa State University $d 2017.
504 $a Includes bibliographical references
520 $a Markov chain Monte Carlo (MCMC) is a computational statistical approach for numerically approximating distributional quantities useful for inference that might otherwise be intractable to directly calculate. A challenge with MCMC methods is developing implementations which are both statistically rigorous and computationally scalable to large data sets. This work generally aims to bridge these aspects by exploiting conditional independence, or Markov structures, in data models. Chapter 2 investigates the model properties and Bayesian fitting of a graph model with Markovian dependence used in deep machine learning and image classification, called a restricted Bolzmann machine (RBM), and Chapter 3 presents a framework for describing inherent instability in a general class of models which includes RBMs. Chapters 4 and 5 introduce a fast method for simulating data from a Markov Random Field (MRF) by exploiting conditional independence specified in the model and a flexible R package that implements the approach in C++.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Iowa State University. $b Statistics. $3 1182662
773 0 $t Dissertation Abstracts International $g 79-02B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10605132 $z click for full text (PQDT)