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You searched for +publisher:"University of Illinois – Urbana-Champaign" +contributor:("Chen, Yuguo"). Showing records 1 – 18 of 18 total matches.

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University of Illinois – Urbana-Champaign

1. Cui, Na. Contributions to modeling parasite dynamics and dimension reduction.

Degree: PhD, 0329, 2012, University of Illinois – Urbana-Champaign

 For my thesis, I have worked on two projects: modeling parasite dynamics (Chapter 2) and complementary dimensionality analysis (Chapter 3). In the first project, we… (more)

Subjects/Keywords: Bayesian hierarchical model; Infection rate; Markov chain Monte Carlo; Panel data; Dimension reduction; Fisher discriminant analysis

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APA (6th Edition):

Cui, N. (2012). Contributions to modeling parasite dynamics and dimension reduction. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/32062

Chicago Manual of Style (16th Edition):

Cui, Na. “Contributions to modeling parasite dynamics and dimension reduction.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/32062.

MLA Handbook (7th Edition):

Cui, Na. “Contributions to modeling parasite dynamics and dimension reduction.” 2012. Web. 23 Feb 2019.

Vancouver:

Cui N. Contributions to modeling parasite dynamics and dimension reduction. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/32062.

Council of Science Editors:

Cui N. Contributions to modeling parasite dynamics and dimension reduction. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/32062


University of Illinois – Urbana-Champaign

2. Zhang, Jingfei. Statistical inference on network data.

Degree: PhD, 0329, 2014, University of Illinois – Urbana-Champaign

 Networks arise from modeling complex systems in various fields, such as computer science, social science, biology, psychology and finance. Understanding and analyzing networks help us… (more)

Subjects/Keywords: Network Inference; Random Graphs; Sequential Importance Sampling; Network Robustness; Exponential Random Graph Model; Dense Subgraph Discovery; Simulated Annealing; Community Detection; Degree Corrected Stochastic Block Model

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APA (6th Edition):

Zhang, J. (2014). Statistical inference on network data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/49840

Chicago Manual of Style (16th Edition):

Zhang, Jingfei. “Statistical inference on network data.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/49840.

MLA Handbook (7th Edition):

Zhang, Jingfei. “Statistical inference on network data.” 2014. Web. 23 Feb 2019.

Vancouver:

Zhang J. Statistical inference on network data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/49840.

Council of Science Editors:

Zhang J. Statistical inference on network data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/49840


University of Illinois – Urbana-Champaign

3. Eisinger, Robert David. Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables.

Degree: PhD, Statistics, 2016, University of Illinois – Urbana-Champaign

 We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals… (more)

Subjects/Keywords: Monte Carlo method; Sequential importance sampling; Counting problem; Contingency Table

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APA (6th Edition):

Eisinger, R. D. (2016). Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92928

Chicago Manual of Style (16th Edition):

Eisinger, Robert David. “Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/92928.

MLA Handbook (7th Edition):

Eisinger, Robert David. “Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables.” 2016. Web. 23 Feb 2019.

Vancouver:

Eisinger RD. Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/92928.

Council of Science Editors:

Eisinger RD. Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92928


University of Illinois – Urbana-Champaign

4. Paul, Subhadeep. Consistent community detection in uni-layer and multi-layer networks.

Degree: PhD, Statistics, 2017, University of Illinois – Urbana-Champaign

 Over the last two decades, we have witnessed a massive explosion of our data collection abilities and the birth of a "big data" age. This… (more)

Subjects/Keywords: Community detection; Consistency; Co-regularization; Invariant subspaces; Minimax rates; Multi-layer networks; Multi-layer null models; Multi-layer modularity; Multi-layer stochastic block model; Network analysis; Non-negative matrix factorization; Neuroimaging; Random effects stochastic block model; Stochastic block model; Spectral clustering; Variational expectation-maximization (EM)

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APA (6th Edition):

Paul, S. (2017). Consistent community detection in uni-layer and multi-layer networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/98136

Chicago Manual of Style (16th Edition):

Paul, Subhadeep. “Consistent community detection in uni-layer and multi-layer networks.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/98136.

MLA Handbook (7th Edition):

Paul, Subhadeep. “Consistent community detection in uni-layer and multi-layer networks.” 2017. Web. 23 Feb 2019.

Vancouver:

Paul S. Consistent community detection in uni-layer and multi-layer networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/98136.

Council of Science Editors:

Paul S. Consistent community detection in uni-layer and multi-layer networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/98136


University of Illinois – Urbana-Champaign

5. Sengupta, Srijan. Statistical analysis of networks with community structure and bootstrap methods for big data.

Degree: PhD, Statistics, 2016, University of Illinois – Urbana-Champaign

 This dissertation is divided into two parts, concerning two areas of statistical methodology. The first part of this dissertation concerns statistical analysis of networks with… (more)

Subjects/Keywords: network data; resampling; community structure; big data

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APA (6th Edition):

Sengupta, S. (2016). Statistical analysis of networks with community structure and bootstrap methods for big data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92763

Chicago Manual of Style (16th Edition):

Sengupta, Srijan. “Statistical analysis of networks with community structure and bootstrap methods for big data.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/92763.

MLA Handbook (7th Edition):

Sengupta, Srijan. “Statistical analysis of networks with community structure and bootstrap methods for big data.” 2016. Web. 23 Feb 2019.

Vancouver:

Sengupta S. Statistical analysis of networks with community structure and bootstrap methods for big data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/92763.

Council of Science Editors:

Sengupta S. Statistical analysis of networks with community structure and bootstrap methods for big data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92763


University of Illinois – Urbana-Champaign

6. He, Yifeng. Inference of high-dimensional linear models with time-varying coefficients.

Degree: PhD, Statistics, 2018, University of Illinois – Urbana-Champaign

 In part 1, we propose a pointwise inference algorithm for high-dimensional linear models with time-varying coefficients and dependent error processes. The method is based on… (more)

Subjects/Keywords: High Dimension; Lasso; Ridge Regression; Time Series; Time Varying Coefficient Models; Kronecker; Precision Matrix; Graphical Methods; Graphical Lasso

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APA (6th Edition):

He, Y. (2018). Inference of high-dimensional linear models with time-varying coefficients. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102452

Chicago Manual of Style (16th Edition):

He, Yifeng. “Inference of high-dimensional linear models with time-varying coefficients.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/102452.

MLA Handbook (7th Edition):

He, Yifeng. “Inference of high-dimensional linear models with time-varying coefficients.” 2018. Web. 23 Feb 2019.

Vancouver:

He Y. Inference of high-dimensional linear models with time-varying coefficients. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/102452.

Council of Science Editors:

He Y. Inference of high-dimensional linear models with time-varying coefficients. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102452


University of Illinois – Urbana-Champaign

7. Zhang, Pinchao. Database optimization algorithm for empirical potentials.

Degree: PhD, Materials Science & Engr, 2016, University of Illinois – Urbana-Champaign

 The development for accurate and efficient empirical potential models requires years of efforts and is highly intuitional. This study provides an automated, quantitative algorithm to… (more)

Subjects/Keywords: Empirical potential models; Optimization algorithm; Bayesian statistics; Monte Carlo; Genetic algorithm; Titanium oxygen interaction

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APA (6th Edition):

Zhang, P. (2016). Database optimization algorithm for empirical potentials. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90714

Chicago Manual of Style (16th Edition):

Zhang, Pinchao. “Database optimization algorithm for empirical potentials.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/90714.

MLA Handbook (7th Edition):

Zhang, Pinchao. “Database optimization algorithm for empirical potentials.” 2016. Web. 23 Feb 2019.

Vancouver:

Zhang P. Database optimization algorithm for empirical potentials. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/90714.

Council of Science Editors:

Zhang P. Database optimization algorithm for empirical potentials. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90714


University of Illinois – Urbana-Champaign

8. Zhang, Xianyang. Statistical inference for dependent data.

Degree: PhD, 0329, 2013, University of Illinois – Urbana-Champaign

 Functional data Analysis has emerged as an important area of statistics which provides convenient and informative tool for the analysis of data objects of high… (more)

Subjects/Keywords: Functional data; Change-point problem; Two sample problem; Self-normalization; High order expansion; Bootstrap; Empirical likelihood

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APA (6th Edition):

Zhang, X. (2013). Statistical inference for dependent data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/45543

Chicago Manual of Style (16th Edition):

Zhang, Xianyang. “Statistical inference for dependent data.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/45543.

MLA Handbook (7th Edition):

Zhang, Xianyang. “Statistical inference for dependent data.” 2013. Web. 23 Feb 2019.

Vancouver:

Zhang X. Statistical inference for dependent data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/45543.

Council of Science Editors:

Zhang X. Statistical inference for dependent data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/45543


University of Illinois – Urbana-Champaign

9. Liu, Yufei. Statistical modeling of heterogeneous data.

Degree: PhD, 0329, 2013, University of Illinois – Urbana-Champaign

 This dissertation is centered on the modeling of heterogeneous data which is ubiquitous in this digital information age. From the statistical point of view heterogeneous… (more)

Subjects/Keywords: Statistical Learning; Clustering; Non-parametric Bayes; Dirichlet Process; Mixture Model; Heterogeneous Data

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APA (6th Edition):

Liu, Y. (2013). Statistical modeling of heterogeneous data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/45589

Chicago Manual of Style (16th Edition):

Liu, Yufei. “Statistical modeling of heterogeneous data.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/45589.

MLA Handbook (7th Edition):

Liu, Yufei. “Statistical modeling of heterogeneous data.” 2013. Web. 23 Feb 2019.

Vancouver:

Liu Y. Statistical modeling of heterogeneous data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/45589.

Council of Science Editors:

Liu Y. Statistical modeling of heterogeneous data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/45589


University of Illinois – Urbana-Champaign

10. Yang, Yunwen. Bayesian empirical likelihood for quantile regression.

Degree: PhD, 0329, 2012, University of Illinois – Urbana-Champaign

 Bayesian inference provides a flexible way of combiningg data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian… (more)

Subjects/Keywords: Efficiency; Empirical likelihood; High quantiles; Quantile regression; Prior; Posterior.

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APA (6th Edition):

Yang, Y. (2012). Bayesian empirical likelihood for quantile regression. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/29522

Chicago Manual of Style (16th Edition):

Yang, Yunwen. “Bayesian empirical likelihood for quantile regression.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/29522.

MLA Handbook (7th Edition):

Yang, Yunwen. “Bayesian empirical likelihood for quantile regression.” 2012. Web. 23 Feb 2019.

Vancouver:

Yang Y. Bayesian empirical likelihood for quantile regression. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/29522.

Council of Science Editors:

Yang Y. Bayesian empirical likelihood for quantile regression. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/29522


University of Illinois – Urbana-Champaign

11. Ji, Ming. Semi-supervised learning and relevance search on networked data.

Degree: PhD, 0112, 2014, University of Illinois – Urbana-Champaign

 Real-world data entities are often connected by meaningful relationships, forming large-scale networks. With the rapid growth of social networks and online relational data, it is… (more)

Subjects/Keywords: Data Mining; Machine Learning; Semi-supervised Learning; Search; Heterogeneous Networks; Graphs

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APA (6th Edition):

Ji, M. (2014). Semi-supervised learning and relevance search on networked data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46856

Chicago Manual of Style (16th Edition):

Ji, Ming. “Semi-supervised learning and relevance search on networked data.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/46856.

MLA Handbook (7th Edition):

Ji, Ming. “Semi-supervised learning and relevance search on networked data.” 2014. Web. 23 Feb 2019.

Vancouver:

Ji M. Semi-supervised learning and relevance search on networked data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/46856.

Council of Science Editors:

Ji M. Semi-supervised learning and relevance search on networked data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46856


University of Illinois – Urbana-Champaign

12. Yeong, Hoong Chieh. Dimensional reduction in nonlinear estimation of multiscale systems.

Degree: PhD, Aerospace Engineering, 2017, University of Illinois – Urbana-Champaign

 State or signal estimation of stochastic systems based on measurement data is an important problem in many areas of science and engineering. The true signal… (more)

Subjects/Keywords: Nonlinear filtering; Homogenization; Stochastic partial differential equation; Particle filter; Maximum likelihood estimation; Mutual information

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APA (6th Edition):

Yeong, H. C. (2017). Dimensional reduction in nonlinear estimation of multiscale systems. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99320

Chicago Manual of Style (16th Edition):

Yeong, Hoong Chieh. “Dimensional reduction in nonlinear estimation of multiscale systems.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/99320.

MLA Handbook (7th Edition):

Yeong, Hoong Chieh. “Dimensional reduction in nonlinear estimation of multiscale systems.” 2017. Web. 23 Feb 2019.

Vancouver:

Yeong HC. Dimensional reduction in nonlinear estimation of multiscale systems. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/99320.

Council of Science Editors:

Yeong HC. Dimensional reduction in nonlinear estimation of multiscale systems. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99320


University of Illinois – Urbana-Champaign

13. Butala, Mark D. A state-space approach to dynamic tomography.

Degree: PhD, 1200, 2011, University of Illinois – Urbana-Champaign

 The statistical inference of a hidden Markov random process is a problem encountered in numerous signal processing applications including dynamic tomography. In dynamic tomography, the… (more)

Subjects/Keywords: tomography; Kalman filtering; Kalman smoothing; ensemble Kalman filtering; ensemble Kalman smoothing; solar tomography; solar corona; coronal electron density; state estimation; dynamic tomography; time-dependent tomography; ensemble Kalman filter convergence; covariance tapering

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APA (6th Edition):

Butala, M. D. (2011). A state-space approach to dynamic tomography. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18480

Chicago Manual of Style (16th Edition):

Butala, Mark D. “A state-space approach to dynamic tomography.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/18480.

MLA Handbook (7th Edition):

Butala, Mark D. “A state-space approach to dynamic tomography.” 2011. Web. 23 Feb 2019.

Vancouver:

Butala MD. A state-space approach to dynamic tomography. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/18480.

Council of Science Editors:

Butala MD. A state-space approach to dynamic tomography. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18480

14. Feng, Yang. Bayesian quantile linear regression.

Degree: PhD, 0329, 2011, University of Illinois – Urbana-Champaign

 Quantile regression, as a supplement to the mean regression, is often used when a comprehensive relationship between the response variable and the explanatory variables is… (more)

Subjects/Keywords: Bayesian inference; Markov chain Monte Carlo (MCMC); Quantile regression; Linearly interpolated density (LID)

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APA (6th Edition):

Feng, Y. (2011). Bayesian quantile linear regression. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/24348

Chicago Manual of Style (16th Edition):

Feng, Yang. “Bayesian quantile linear regression.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/24348.

MLA Handbook (7th Edition):

Feng, Yang. “Bayesian quantile linear regression.” 2011. Web. 23 Feb 2019.

Vancouver:

Feng Y. Bayesian quantile linear regression. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/24348.

Council of Science Editors:

Feng Y. Bayesian quantile linear regression. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/24348

15. Sewell, Daniel K. Statistical models and inference for dynamic networks.

Degree: PhD, Statistics, 2015, University of Illinois – Urbana-Champaign

 Dyadic data are ubiquitous and arise in the fields of biology, epidemiology, sociology, and many more. Such dyadic data are often best understood within the… (more)

Subjects/Keywords: network; dynamic network; latent space; weighted network; social network; edge attraction; prediction; influence; missing data; scalability; stability; Longitudinal data; clustering; community detection

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APA (6th Edition):

Sewell, D. K. (2015). Statistical models and inference for dynamic networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78422

Chicago Manual of Style (16th Edition):

Sewell, Daniel K. “Statistical models and inference for dynamic networks.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/78422.

MLA Handbook (7th Edition):

Sewell, Daniel K. “Statistical models and inference for dynamic networks.” 2015. Web. 23 Feb 2019.

Vancouver:

Sewell DK. Statistical models and inference for dynamic networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/78422.

Council of Science Editors:

Sewell DK. Statistical models and inference for dynamic networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78422

16. Yun, Jong Hyun. Ensemble filtering for state space models.

Degree: PhD, 0329, 2012, University of Illinois – Urbana-Champaign

 The state space model has been widely used in various fields including economics, finance, bioinformatics, oceanography, and tomography. The goal of the filtering problem is… (more)

Subjects/Keywords: Nonlinear filtering; Sequential Monte Carlo; Particle filter; Ensemble Kalman filter; State space model; Target tracking; Augmented particle filter; Localized augmented particle filter; Particle Monte Carlo Markov chain; Lorenz model; Particle filtering with independent batches.

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APA (6th Edition):

Yun, J. H. (2012). Ensemble filtering for state space models. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/34574

Chicago Manual of Style (16th Edition):

Yun, Jong Hyun. “Ensemble filtering for state space models.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/34574.

MLA Handbook (7th Edition):

Yun, Jong Hyun. “Ensemble filtering for state space models.” 2012. Web. 23 Feb 2019.

Vancouver:

Yun JH. Ensemble filtering for state space models. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/34574.

Council of Science Editors:

Yun JH. Ensemble filtering for state space models. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/34574

17. Xu, Jianfeng. Bayesian Latent Class Models.

Degree: PhD, 0329, 2011, University of Illinois – Urbana-Champaign

 The latent class model (LCM) is a statistical method that introduces a set of latent categorical variables. The main advantage of LCM is that conditional… (more)

Subjects/Keywords: Latent Class Model; Bayesian; magnetic resonance imaging (MRI); Co-segmentation; Markov Random Field; Market Segmentation; Sparsity; Collaborative; Multiple Partition Process; Dirichlet Process; Indian Buffet Process; Array Comparative genomic hybridization (ACGH)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xu, J. (2011). Bayesian Latent Class Models. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/24418

Chicago Manual of Style (16th Edition):

Xu, Jianfeng. “Bayesian Latent Class Models.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/24418.

MLA Handbook (7th Edition):

Xu, Jianfeng. “Bayesian Latent Class Models.” 2011. Web. 23 Feb 2019.

Vancouver:

Xu J. Bayesian Latent Class Models. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/24418.

Council of Science Editors:

Xu J. Bayesian Latent Class Models. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/24418

18. Zhang, Lei. Sensor development for estimation of biomass yield applied to Miscanthus Giganteus.

Degree: PhD, 5163, 2011, University of Illinois – Urbana-Champaign

 Precision Agriculture technologies such as yield monitoring have been available for traditional field crops for decades. However, there are currently none available for energy crops… (more)

Subjects/Keywords: Miscanthus Giganteus; yield monitoring; sensor; Light Detection and Ranging (LIDAR); machine vision; Monte Carlo

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, L. (2011). Sensor development for estimation of biomass yield applied to Miscanthus Giganteus. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/26286

Chicago Manual of Style (16th Edition):

Zhang, Lei. “Sensor development for estimation of biomass yield applied to Miscanthus Giganteus.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 23, 2019. http://hdl.handle.net/2142/26286.

MLA Handbook (7th Edition):

Zhang, Lei. “Sensor development for estimation of biomass yield applied to Miscanthus Giganteus.” 2011. Web. 23 Feb 2019.

Vancouver:

Zhang L. Sensor development for estimation of biomass yield applied to Miscanthus Giganteus. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2019 Feb 23]. Available from: http://hdl.handle.net/2142/26286.

Council of Science Editors:

Zhang L. Sensor development for estimation of biomass yield applied to Miscanthus Giganteus. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/26286

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