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You searched for +publisher:"Rice University" +contributor:("Vannucci, Marina"). Showing records 1 – 14 of 14 total matches.

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Rice University

1. Chiang, Sharon. Hierarchical Bayesian Models for Multimodal Neuroimaging Data.

Degree: PhD, Engineering, 2016, Rice University

 Within the past few decades, advances in imaging acquisition have given rise to a large number of in vivo techniques for brain mapping. This wide… (more)

Subjects/Keywords: Bayesian hierarchical model; Positron emission tomography (PET); Functional magnetic resonance imaging (fMRI); Structural MRI; Spatially-informed prior; Mixture model; Variable selection; Polya-Gamma distribution; Vector Autoregressive (VAR) model; Granger Causality; Effective Connectivity; Multimodal Neuroimaging; Temporal Lobe Epilepsy

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APA (6th Edition):

Chiang, S. (2016). Hierarchical Bayesian Models for Multimodal Neuroimaging Data. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/96258

Chicago Manual of Style (16th Edition):

Chiang, Sharon. “Hierarchical Bayesian Models for Multimodal Neuroimaging Data.” 2016. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/96258.

MLA Handbook (7th Edition):

Chiang, Sharon. “Hierarchical Bayesian Models for Multimodal Neuroimaging Data.” 2016. Web. 21 Nov 2019.

Vancouver:

Chiang S. Hierarchical Bayesian Models for Multimodal Neuroimaging Data. [Internet] [Doctoral dissertation]. Rice University; 2016. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/96258.

Council of Science Editors:

Chiang S. Hierarchical Bayesian Models for Multimodal Neuroimaging Data. [Doctoral Dissertation]. Rice University; 2016. Available from: http://hdl.handle.net/1911/96258


Rice University

2. Kenney, Colleen. On the separation of T Tauri star spectra using non-negative matrix factorization and Bayesian positive source separation.

Degree: PhD, Natural Sciences, 2010, Rice University

 The objective of this study is to compare and evaluate Bayesian and deterministic methods of positive source separation of young star spectra. In the Bayesian… (more)

Subjects/Keywords: Statistics

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APA (6th Edition):

Kenney, C. (2010). On the separation of T Tauri star spectra using non-negative matrix factorization and Bayesian positive source separation. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/62077

Chicago Manual of Style (16th Edition):

Kenney, Colleen. “On the separation of T Tauri star spectra using non-negative matrix factorization and Bayesian positive source separation.” 2010. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/62077.

MLA Handbook (7th Edition):

Kenney, Colleen. “On the separation of T Tauri star spectra using non-negative matrix factorization and Bayesian positive source separation.” 2010. Web. 21 Nov 2019.

Vancouver:

Kenney C. On the separation of T Tauri star spectra using non-negative matrix factorization and Bayesian positive source separation. [Internet] [Doctoral dissertation]. Rice University; 2010. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/62077.

Council of Science Editors:

Kenney C. On the separation of T Tauri star spectra using non-negative matrix factorization and Bayesian positive source separation. [Doctoral Dissertation]. Rice University; 2010. Available from: http://hdl.handle.net/1911/62077


Rice University

3. Savitsky, Terrance D. Generalized Gaussian process models with Bayesian variable selection.

Degree: PhD, Natural Sciences, 2010, Rice University

 This research proposes a unified Gaussian process modeling approach that extends to data from the exponential dispersion family and survival data. Our specific interest is… (more)

Subjects/Keywords: Statistics

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APA (6th Edition):

Savitsky, T. D. (2010). Generalized Gaussian process models with Bayesian variable selection. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/62083

Chicago Manual of Style (16th Edition):

Savitsky, Terrance D. “Generalized Gaussian process models with Bayesian variable selection.” 2010. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/62083.

MLA Handbook (7th Edition):

Savitsky, Terrance D. “Generalized Gaussian process models with Bayesian variable selection.” 2010. Web. 21 Nov 2019.

Vancouver:

Savitsky TD. Generalized Gaussian process models with Bayesian variable selection. [Internet] [Doctoral dissertation]. Rice University; 2010. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/62083.

Council of Science Editors:

Savitsky TD. Generalized Gaussian process models with Bayesian variable selection. [Doctoral Dissertation]. Rice University; 2010. Available from: http://hdl.handle.net/1911/62083


Rice University

4. Wadsworth, W Duncan. Bayesian Methods for the Analysis of Microbiome Data.

Degree: PhD, Engineering, 2016, Rice University

 Bacteria, archaea, viruses, and fungi are present in large numbers both on and inside of our bodies. On average, only one in ten of “our”… (more)

Subjects/Keywords: Bayesian hierarchical model; Data integration; Dirichlet-Multinomial; Microbiome data; Variable selection; Multiple testing; Bayesian nonparametrics

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APA (6th Edition):

Wadsworth, W. D. (2016). Bayesian Methods for the Analysis of Microbiome Data. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/95977

Chicago Manual of Style (16th Edition):

Wadsworth, W Duncan. “Bayesian Methods for the Analysis of Microbiome Data.” 2016. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/95977.

MLA Handbook (7th Edition):

Wadsworth, W Duncan. “Bayesian Methods for the Analysis of Microbiome Data.” 2016. Web. 21 Nov 2019.

Vancouver:

Wadsworth WD. Bayesian Methods for the Analysis of Microbiome Data. [Internet] [Doctoral dissertation]. Rice University; 2016. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/95977.

Council of Science Editors:

Wadsworth WD. Bayesian Methods for the Analysis of Microbiome Data. [Doctoral Dissertation]. Rice University; 2016. Available from: http://hdl.handle.net/1911/95977


Rice University

5. Chapple, Andrew Genius. Bayesian Models for Clinical Trials and Survival Analysis.

Degree: PhD, Engineering, 2018, Rice University

 In this Thesis, three Bayesian methods are proposed to tackle problems seen in survival analysis and clinical trials. They use Markov Chain Monte Carlo methods… (more)

Subjects/Keywords: Bayesian Analysis; Survival Analysis; Clinical Trials; Statistics

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APA (6th Edition):

Chapple, A. G. (2018). Bayesian Models for Clinical Trials and Survival Analysis. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/105680

Chicago Manual of Style (16th Edition):

Chapple, Andrew Genius. “Bayesian Models for Clinical Trials and Survival Analysis.” 2018. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/105680.

MLA Handbook (7th Edition):

Chapple, Andrew Genius. “Bayesian Models for Clinical Trials and Survival Analysis.” 2018. Web. 21 Nov 2019.

Vancouver:

Chapple AG. Bayesian Models for Clinical Trials and Survival Analysis. [Internet] [Doctoral dissertation]. Rice University; 2018. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/105680.

Council of Science Editors:

Chapple AG. Bayesian Models for Clinical Trials and Survival Analysis. [Doctoral Dissertation]. Rice University; 2018. Available from: http://hdl.handle.net/1911/105680


Rice University

6. Rogers, Donald. Time Based Bayesian Optimal Interval (TITE-BOIN) Design Algorithm Performance under Weibull Distribution on Simulated Phase I Clinical Trial Data.

Degree: MA, Engineering, 2015, Rice University

 In phase I clinical trials, our goal is to effectively treat the patient while minimizing the chance of exposing them to excessively toxic doses of… (more)

Subjects/Keywords: BOIN; Interval Design; Bayesian

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APA (6th Edition):

Rogers, D. (2015). Time Based Bayesian Optimal Interval (TITE-BOIN) Design Algorithm Performance under Weibull Distribution on Simulated Phase I Clinical Trial Data. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/88113

Chicago Manual of Style (16th Edition):

Rogers, Donald. “Time Based Bayesian Optimal Interval (TITE-BOIN) Design Algorithm Performance under Weibull Distribution on Simulated Phase I Clinical Trial Data.” 2015. Masters Thesis, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/88113.

MLA Handbook (7th Edition):

Rogers, Donald. “Time Based Bayesian Optimal Interval (TITE-BOIN) Design Algorithm Performance under Weibull Distribution on Simulated Phase I Clinical Trial Data.” 2015. Web. 21 Nov 2019.

Vancouver:

Rogers D. Time Based Bayesian Optimal Interval (TITE-BOIN) Design Algorithm Performance under Weibull Distribution on Simulated Phase I Clinical Trial Data. [Internet] [Masters thesis]. Rice University; 2015. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/88113.

Council of Science Editors:

Rogers D. Time Based Bayesian Optimal Interval (TITE-BOIN) Design Algorithm Performance under Weibull Distribution on Simulated Phase I Clinical Trial Data. [Masters Thesis]. Rice University; 2015. Available from: http://hdl.handle.net/1911/88113


Rice University

7. Peterson, Christine. Bayesian graphical models for biological network inference.

Degree: PhD, Engineering, 2013, Rice University

 In this work, we propose approaches for the inference of graphical models in the Bayesian framework. Graphical models, which use a network structure to represent… (more)

Subjects/Keywords: Statistics; Graphical models; Bayesian inference; Informative priors; Biological networks

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APA (6th Edition):

Peterson, C. (2013). Bayesian graphical models for biological network inference. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/77444

Chicago Manual of Style (16th Edition):

Peterson, Christine. “Bayesian graphical models for biological network inference.” 2013. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/77444.

MLA Handbook (7th Edition):

Peterson, Christine. “Bayesian graphical models for biological network inference.” 2013. Web. 21 Nov 2019.

Vancouver:

Peterson C. Bayesian graphical models for biological network inference. [Internet] [Doctoral dissertation]. Rice University; 2013. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/77444.

Council of Science Editors:

Peterson C. Bayesian graphical models for biological network inference. [Doctoral Dissertation]. Rice University; 2013. Available from: http://hdl.handle.net/1911/77444


Rice University

8. Berliner, Nathan K. Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness.

Degree: MA, Engineering, 2015, Rice University

 In this thesis multiple imputation, survival analysis, and propensity score analysis are combined in order to answer questions about treatment efficacy in cancer data with… (more)

Subjects/Keywords: Multiple Imputation; Survival Analysis; Causal Analysis

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APA (6th Edition):

Berliner, N. K. (2015). Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/87747

Chicago Manual of Style (16th Edition):

Berliner, Nathan K. “Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness.” 2015. Masters Thesis, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/87747.

MLA Handbook (7th Edition):

Berliner, Nathan K. “Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness.” 2015. Web. 21 Nov 2019.

Vancouver:

Berliner NK. Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness. [Internet] [Masters thesis]. Rice University; 2015. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/87747.

Council of Science Editors:

Berliner NK. Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness. [Masters Thesis]. Rice University; 2015. Available from: http://hdl.handle.net/1911/87747

9. Li, Qiwei. Bayesian Models for High-Dimensional Count Data with Feature Selection.

Degree: PhD, Engineering, 2016, Rice University

 Modern big data analytics often involve large data sets in which the features of interest are measured as counts. My thesis considers the problem of… (more)

Subjects/Keywords: Statistics; Bayesian inference; High-dimensional data; Count data; Clustering; Feature selection; Regression; Integrative analysis; Bayesian nonparametric approaches; Dirichlet process; Markov chain Monte Carlos; Graphical network priors; Markov random field

…U.S.A.) and Marina Vannucci (Rice University, U.S.A.). In Chapter 3, I propose… …Rice University, U.S.A.). I conclude the thesis with Chapter 4, which summarizes the two… 

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APA (6th Edition):

Li, Q. (2016). Bayesian Models for High-Dimensional Count Data with Feature Selection. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/95966

Chicago Manual of Style (16th Edition):

Li, Qiwei. “Bayesian Models for High-Dimensional Count Data with Feature Selection.” 2016. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/95966.

MLA Handbook (7th Edition):

Li, Qiwei. “Bayesian Models for High-Dimensional Count Data with Feature Selection.” 2016. Web. 21 Nov 2019.

Vancouver:

Li Q. Bayesian Models for High-Dimensional Count Data with Feature Selection. [Internet] [Doctoral dissertation]. Rice University; 2016. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/95966.

Council of Science Editors:

Li Q. Bayesian Models for High-Dimensional Count Data with Feature Selection. [Doctoral Dissertation]. Rice University; 2016. Available from: http://hdl.handle.net/1911/95966

10. Waters, Andrew. Bayesian Methods for Learning Analytics.

Degree: PhD, Engineering, 2014, Rice University

 Learning Analytics (LA) is a broad umbrella term used to describe statistical models and algorithms for understanding the relationship be- tween a set of learners… (more)

Subjects/Keywords: Bayesian methods; Learning analytics; Sparse factor analysis; Collaboration detection; Machine learning

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APA (6th Edition):

Waters, A. (2014). Bayesian Methods for Learning Analytics. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/77569

Chicago Manual of Style (16th Edition):

Waters, Andrew. “Bayesian Methods for Learning Analytics.” 2014. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/77569.

MLA Handbook (7th Edition):

Waters, Andrew. “Bayesian Methods for Learning Analytics.” 2014. Web. 21 Nov 2019.

Vancouver:

Waters A. Bayesian Methods for Learning Analytics. [Internet] [Doctoral dissertation]. Rice University; 2014. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/77569.

Council of Science Editors:

Waters A. Bayesian Methods for Learning Analytics. [Doctoral Dissertation]. Rice University; 2014. Available from: http://hdl.handle.net/1911/77569

11. Zhang, Linlin. Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data.

Degree: PhD, Engineering, 2015, Rice University

 In this research work, I propose Bayesian nonparametric approaches to model functional magnetic resonance imaging (fMRI) data. Due to the complex spatial and temporal correlation… (more)

Subjects/Keywords: Bayesian nonparametric approaches; functional magnetic resonance imaging (fMRI) data; spatio-temporal correlation; activation detection; brain connectivity; graphical network priors; variational Bayesian; Markov Chain Monte Carlo (MCMC)

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APA (6th Edition):

Zhang, L. (2015). Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/88406

Chicago Manual of Style (16th Edition):

Zhang, Linlin. “Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data.” 2015. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/88406.

MLA Handbook (7th Edition):

Zhang, Linlin. “Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data.” 2015. Web. 21 Nov 2019.

Vancouver:

Zhang L. Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data. [Internet] [Doctoral dissertation]. Rice University; 2015. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/88406.

Council of Science Editors:

Zhang L. Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data. [Doctoral Dissertation]. Rice University; 2015. Available from: http://hdl.handle.net/1911/88406

12. Heinrich, Tobias. Strategic Choices in Foreign Aid.

Degree: PhD, Social Sciences, 2013, Rice University

 This dissertation addresses three important questions surrounding the politics of foreign aid, namely what leads to its provisions by donor countries, and what are some… (more)

Subjects/Keywords: International relations; Foreign aid; Development; Foreign policy

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APA (6th Edition):

Heinrich, T. (2013). Strategic Choices in Foreign Aid. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/71963

Chicago Manual of Style (16th Edition):

Heinrich, Tobias. “Strategic Choices in Foreign Aid.” 2013. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/71963.

MLA Handbook (7th Edition):

Heinrich, Tobias. “Strategic Choices in Foreign Aid.” 2013. Web. 21 Nov 2019.

Vancouver:

Heinrich T. Strategic Choices in Foreign Aid. [Internet] [Doctoral dissertation]. Rice University; 2013. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/71963.

Council of Science Editors:

Heinrich T. Strategic Choices in Foreign Aid. [Doctoral Dissertation]. Rice University; 2013. Available from: http://hdl.handle.net/1911/71963

13. Liu, Kun. Discontinuous Galerkin Methods for Parabolic Partial Differential Equations with Random Input Data.

Degree: PhD, Engineering, 2013, Rice University

 This thesis discusses and develops one approach to solve parabolic partial differential equations with random input data. The stochastic problem is firstly transformed into a… (more)

Subjects/Keywords: Parabolic PDEs; Monte Carlo Discontinuous Galerkin; Locally mass conservation; Random input data; Kernel PCA; Random permeability; Darcy's Law; Coupled flow and transport

…Research Computing Support Group at Rice University is used to do Monte Carlo simulations. A… 

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APA (6th Edition):

Liu, K. (2013). Discontinuous Galerkin Methods for Parabolic Partial Differential Equations with Random Input Data. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/71989

Chicago Manual of Style (16th Edition):

Liu, Kun. “Discontinuous Galerkin Methods for Parabolic Partial Differential Equations with Random Input Data.” 2013. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/71989.

MLA Handbook (7th Edition):

Liu, Kun. “Discontinuous Galerkin Methods for Parabolic Partial Differential Equations with Random Input Data.” 2013. Web. 21 Nov 2019.

Vancouver:

Liu K. Discontinuous Galerkin Methods for Parabolic Partial Differential Equations with Random Input Data. [Internet] [Doctoral dissertation]. Rice University; 2013. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/71989.

Council of Science Editors:

Liu K. Discontinuous Galerkin Methods for Parabolic Partial Differential Equations with Random Input Data. [Doctoral Dissertation]. Rice University; 2013. Available from: http://hdl.handle.net/1911/71989

14. Cox, Tod. Essays On Discrete Choice Models.

Degree: PhD, Business, 2014, Rice University

 Discrete choice models have long been a cornerstone of marketing research. The beginning of what is currently known as the Multinomial Probit Model was published… (more)

Subjects/Keywords: Discrete choice models; Multinomial logit; Multinomial probit; Elimination by aspects; B2B Branding; Venture capital; Conjoint analysis

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APA (6th Edition):

Cox, T. (2014). Essays On Discrete Choice Models. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/76364

Chicago Manual of Style (16th Edition):

Cox, Tod. “Essays On Discrete Choice Models.” 2014. Doctoral Dissertation, Rice University. Accessed November 21, 2019. http://hdl.handle.net/1911/76364.

MLA Handbook (7th Edition):

Cox, Tod. “Essays On Discrete Choice Models.” 2014. Web. 21 Nov 2019.

Vancouver:

Cox T. Essays On Discrete Choice Models. [Internet] [Doctoral dissertation]. Rice University; 2014. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/1911/76364.

Council of Science Editors:

Cox T. Essays On Discrete Choice Models. [Doctoral Dissertation]. Rice University; 2014. Available from: http://hdl.handle.net/1911/76364

.