東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Some Bayesian and multivariate analy...

Zhou, Wen.

FindBook

Google Book

Amazon

博客來

Some Bayesian and multivariate analysis methods in statistical machine learning and applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Some Bayesian and multivariate analysis methods in statistical machine learning and applications./
作者:	Zhou, Wen.
面頁冊數:	177 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:	Dissertation Abstracts International75-10B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3627538
ISBN:	9781321028683

Some Bayesian and multivariate analysis methods in statistical machine learning and applications.
Zhou, Wen.

Some Bayesian and multivariate analysis methods in statistical machine learning and applications. - 177 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.

Thesis (Ph.D.)--Iowa State University, 2014.

This item must not be sold to any third party vendors.

In this dissertation, we consider some Bayesian and multivariate analysis methods in statistical machine learning as well as some applications of Bayesian methodology with differential equation models to study dynamics during co-infections by Leishmania major and Leishmania amazonensis based on longitudinal data.

ISBN: 9781321028683Subjects--Topical Terms:

517247
Statistics.

Some Bayesian and multivariate analysis methods in statistical machine learning and applications.
LDR:03045nmm a2200325 4500 001 1987989
005 20150716112154.5
008 150803s2014 ||||||||||||||||| ||eng d
020 $a 9781321028683
035 $a (MiAaPQ)AAI3627538
035 $a AAI3627538
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhou, Wen. $3 2122813
245 1 0 $a Some Bayesian and multivariate analysis methods in statistical machine learning and applications.
300 $a 177 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
500 $a Advisers: Stephen Vardeman; Huaiqing Wu.
502 $a Thesis (Ph.D.)--Iowa State University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a In this dissertation, we consider some Bayesian and multivariate analysis methods in statistical machine learning as well as some applications of Bayesian methodology with differential equation models to study dynamics during co-infections by Leishmania major and Leishmania amazonensis based on longitudinal data.
520 $a First, we developed a new MCMC algorithm to integrate the curvature information of a target distribution to sample the target distribution accurately and efficiently. We then introduced a Bayesian Hierarchical Topographic Clustering method (BHTC) motivated by the well-known self-organizing map (SOM) using stationary isotropic Gaussian processes and principal component approximations. We constructed a computationally tractable MCMC algorithm to sample posterior distributions of the covariance matrices, as well as the posterior distributions of remaining BHTC parameters. To summarize the posterior distributions of BHTC parameters in a coherent fashion for the purpose of data clustering, we adopted a posterior risk framework that accounts for both data partitioning and topographic preservation.
520 $a We also proposed a classification method based on the weighted bootstrap and ensemble mechanism to deal with covariate shifts in classifications, the Active Set Selections based Classification (ASSC). This procedure is flexible to be combined with classification methods including support vector machine (SVM), classification trees, and Fisher's discriminant classifier (LDA) etc. to improve their performances.
520 $a We adopted Bayesian methodologies to study longitudinal data from co-infections by Leishmania major and Leishmania amazonensis. In the proposed Bayesian analysis, we modeled the immunobiological dynamics and data variations by Lotka-Volterra equations and the linear mixed model, respectively. Using the posterior distributions of differential equation parameters and the concept of asymptotic stable equilibrium of differential equations, we successfully quantified the immune efficiency.
590 $a School code: 0097.
650 4 $a Statistics. $3 517247
650 4 $a Applied mathematics. $3 2122814
690 $a 0463
690 $a 0364
710 2 $a Iowa State University. $b Statistics. $3 1020860
773 0 $t Dissertation Abstracts International $g 75-10B(E).
790 $a 0097
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3627538