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You searched for subject:(nonparametric Bayesian). Showing records 1 – 30 of 108 total matches.

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University of Georgia

1. Yang, Ying. Nonparametric Bayesian inference in biostatistics.

Degree: PhD, Statistics, 2004, University of Georgia

 Traditional parametric linear models are subject to several limiting constraints. In biomedical data analysis, parametric assumptions are often inappropriate because of multimodality and skewness arising… (more)

Subjects/Keywords: Nonparametric Bayesian

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

APA (6th Edition):

Yang, Y. (2004). Nonparametric Bayesian inference in biostatistics. (Doctoral Dissertation). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/yang_ying_200412_phd

Chicago Manual of Style (16th Edition):

Yang, Ying. “Nonparametric Bayesian inference in biostatistics.” 2004. Doctoral Dissertation, University of Georgia. Accessed October 19, 2019. http://purl.galileo.usg.edu/uga_etd/yang_ying_200412_phd.

MLA Handbook (7th Edition):

Yang, Ying. “Nonparametric Bayesian inference in biostatistics.” 2004. Web. 19 Oct 2019.

Vancouver:

Yang Y. Nonparametric Bayesian inference in biostatistics. [Internet] [Doctoral dissertation]. University of Georgia; 2004. [cited 2019 Oct 19]. Available from: http://purl.galileo.usg.edu/uga_etd/yang_ying_200412_phd.

Council of Science Editors:

Yang Y. Nonparametric Bayesian inference in biostatistics. [Doctoral Dissertation]. University of Georgia; 2004. Available from: http://purl.galileo.usg.edu/uga_etd/yang_ying_200412_phd


Texas A&M University

2. Shin, Minsuk. Priors for Bayesian Shrinkage and High-Dimensional Model Selection.

Degree: PhD, Statistics, 2017, Texas A&M University

 This dissertation focuses on the choice of priors in Bayesian model selection and their applied, theoretical and computational aspects. As George Box famously said ?all… (more)

Subjects/Keywords: Bayesian model selection; Nonparametric model

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APA (6th Edition):

Shin, M. (2017). Priors for Bayesian Shrinkage and High-Dimensional Model Selection. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/166096

Chicago Manual of Style (16th Edition):

Shin, Minsuk. “Priors for Bayesian Shrinkage and High-Dimensional Model Selection.” 2017. Doctoral Dissertation, Texas A&M University. Accessed October 19, 2019. http://hdl.handle.net/1969.1/166096.

MLA Handbook (7th Edition):

Shin, Minsuk. “Priors for Bayesian Shrinkage and High-Dimensional Model Selection.” 2017. Web. 19 Oct 2019.

Vancouver:

Shin M. Priors for Bayesian Shrinkage and High-Dimensional Model Selection. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1969.1/166096.

Council of Science Editors:

Shin M. Priors for Bayesian Shrinkage and High-Dimensional Model Selection. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/166096


Duke University

3. Christensen, Jonathan. Applications and Computation of Stateful Polya Trees .

Degree: 2017, Duke University

  Polya trees are a class of nonparametric priors on distributions which are able to model absolutely continuous distributions directly, rather than modeling a discrete… (more)

Subjects/Keywords: Statistics; Bayesian; Nonparametric; Polya tree

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APA (6th Edition):

Christensen, J. (2017). Applications and Computation of Stateful Polya Trees . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16370

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Christensen, Jonathan. “Applications and Computation of Stateful Polya Trees .” 2017. Thesis, Duke University. Accessed October 19, 2019. http://hdl.handle.net/10161/16370.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Christensen, Jonathan. “Applications and Computation of Stateful Polya Trees .” 2017. Web. 19 Oct 2019.

Vancouver:

Christensen J. Applications and Computation of Stateful Polya Trees . [Internet] [Thesis]. Duke University; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10161/16370.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Christensen J. Applications and Computation of Stateful Polya Trees . [Thesis]. Duke University; 2017. Available from: http://hdl.handle.net/10161/16370

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Tech

4. Amini Moghadam, Shahram. Model Uncertainty & Model Averaging Techniques.

Degree: PhD, Economics (Science), 2012, Virginia Tech

 The primary aim of this research is to shed more light on the issue of model uncertainty in applied econometrics in general and cross-country growth… (more)

Subjects/Keywords: Frequentist; Bayesian; Jackknife; Model Averaging; Mallows; Nonparametric

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

APA (6th Edition):

Amini Moghadam, S. (2012). Model Uncertainty & Model Averaging Techniques. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28398

Chicago Manual of Style (16th Edition):

Amini Moghadam, Shahram. “Model Uncertainty & Model Averaging Techniques.” 2012. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/28398.

MLA Handbook (7th Edition):

Amini Moghadam, Shahram. “Model Uncertainty & Model Averaging Techniques.” 2012. Web. 19 Oct 2019.

Vancouver:

Amini Moghadam S. Model Uncertainty & Model Averaging Techniques. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/28398.

Council of Science Editors:

Amini Moghadam S. Model Uncertainty & Model Averaging Techniques. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/28398


McMaster University

5. Thompson, John R.J. A Monte Carlo Investigation of Smoothing Methods for Error Density Estimation in Functional Data Analysis with an Illustrative Application to a Chemometric Data Set.

Degree: MSc, 2014, McMaster University

Functional data analysis is a eld in statistics that analyzes data which are dependent on time or space and from which inference can be conducted.… (more)

Subjects/Keywords: Nonparametric; Bayesian; Smoothing; Splines; Kernel; Spectrometric data

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APA (6th Edition):

Thompson, J. R. J. (2014). A Monte Carlo Investigation of Smoothing Methods for Error Density Estimation in Functional Data Analysis with an Illustrative Application to a Chemometric Data Set. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/16575

Chicago Manual of Style (16th Edition):

Thompson, John R J. “A Monte Carlo Investigation of Smoothing Methods for Error Density Estimation in Functional Data Analysis with an Illustrative Application to a Chemometric Data Set.” 2014. Masters Thesis, McMaster University. Accessed October 19, 2019. http://hdl.handle.net/11375/16575.

MLA Handbook (7th Edition):

Thompson, John R J. “A Monte Carlo Investigation of Smoothing Methods for Error Density Estimation in Functional Data Analysis with an Illustrative Application to a Chemometric Data Set.” 2014. Web. 19 Oct 2019.

Vancouver:

Thompson JRJ. A Monte Carlo Investigation of Smoothing Methods for Error Density Estimation in Functional Data Analysis with an Illustrative Application to a Chemometric Data Set. [Internet] [Masters thesis]. McMaster University; 2014. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/11375/16575.

Council of Science Editors:

Thompson JRJ. A Monte Carlo Investigation of Smoothing Methods for Error Density Estimation in Functional Data Analysis with an Illustrative Application to a Chemometric Data Set. [Masters Thesis]. McMaster University; 2014. Available from: http://hdl.handle.net/11375/16575


University of New South Wales

6. Lin, Peng. Bayesian Nonparametric Approaches for Modelling Stochastic Temporal Events.

Degree: Computer Science & Engineering, 2017, University of New South Wales

 Modelling stochastic temporal events is a classic machine learning problem that has drawn enormous research attentions over recent decades. Traditional approaches heavily focused on the… (more)

Subjects/Keywords: Dirichlet process; Bayesian nonparametric; Hawkes process

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APA (6th Edition):

Lin, P. (2017). Bayesian Nonparametric Approaches for Modelling Stochastic Temporal Events. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58764 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47509/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Lin, Peng. “Bayesian Nonparametric Approaches for Modelling Stochastic Temporal Events.” 2017. Doctoral Dissertation, University of New South Wales. Accessed October 19, 2019. http://handle.unsw.edu.au/1959.4/58764 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47509/SOURCE02?view=true.

MLA Handbook (7th Edition):

Lin, Peng. “Bayesian Nonparametric Approaches for Modelling Stochastic Temporal Events.” 2017. Web. 19 Oct 2019.

Vancouver:

Lin P. Bayesian Nonparametric Approaches for Modelling Stochastic Temporal Events. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2019 Oct 19]. Available from: http://handle.unsw.edu.au/1959.4/58764 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47509/SOURCE02?view=true.

Council of Science Editors:

Lin P. Bayesian Nonparametric Approaches for Modelling Stochastic Temporal Events. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/58764 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47509/SOURCE02?view=true


Tampere University

7. Lu, Chien. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .

Degree: 2018, Tampere University

 Entropy estimation is an important technique to summarize the uncertainty of a distribution underlying a set of samples. It ties to important research problems in… (more)

Subjects/Keywords: entropy estimation; nonparametric estimator; Bayesian inference

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

APA (6th Edition):

Lu, C. (2018). An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/104066

Chicago Manual of Style (16th Edition):

Lu, Chien. “An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .” 2018. Masters Thesis, Tampere University. Accessed October 19, 2019. https://trepo.tuni.fi/handle/10024/104066.

MLA Handbook (7th Edition):

Lu, Chien. “An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .” 2018. Web. 19 Oct 2019.

Vancouver:

Lu C. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . [Internet] [Masters thesis]. Tampere University; 2018. [cited 2019 Oct 19]. Available from: https://trepo.tuni.fi/handle/10024/104066.

Council of Science Editors:

Lu C. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . [Masters Thesis]. Tampere University; 2018. Available from: https://trepo.tuni.fi/handle/10024/104066


Duke University

8. PETRALIA, FRANCESCA. Structured Bayesian learning through mixture models .

Degree: 2013, Duke University

  In this thesis, we develop some Bayesian mixture density estimation for univariate and multivariate data. We start proposing a repulsive process favoring mixture components… (more)

Subjects/Keywords: Statistics; Bayesian density estimation; Bayesian Nonparametric; Mixture Models

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APA (6th Edition):

PETRALIA, F. (2013). Structured Bayesian learning through mixture models . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/8065

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

PETRALIA, FRANCESCA. “Structured Bayesian learning through mixture models .” 2013. Thesis, Duke University. Accessed October 19, 2019. http://hdl.handle.net/10161/8065.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

PETRALIA, FRANCESCA. “Structured Bayesian learning through mixture models .” 2013. Web. 19 Oct 2019.

Vancouver:

PETRALIA F. Structured Bayesian learning through mixture models . [Internet] [Thesis]. Duke University; 2013. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10161/8065.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

PETRALIA F. Structured Bayesian learning through mixture models . [Thesis]. Duke University; 2013. Available from: http://hdl.handle.net/10161/8065

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Southern California

9. Kryshchenko (Chubatiuk), Alona. Nonparametric estimation of an unknown probability distribution using maximum likelihood and Bayesian approaches.

Degree: PhD, Applied Mathematics, 2013, University of Southern California

 Suppose we observe a random sample Y₁,...,YN, of independent but not necessarily identically distributed random variables Yᵢ,∈ ℝᵈ, for i = 1,...,N. Assume also that… (more)

Subjects/Keywords: statistics; nonparametric population analysis; applied statistics; population pharmacokinetics theory; nonparametric maximum likelihood estimation; nonparametric Bayesian estimation

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APA (6th Edition):

Kryshchenko (Chubatiuk), A. (2013). Nonparametric estimation of an unknown probability distribution using maximum likelihood and Bayesian approaches. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/309797/rec/4448

Chicago Manual of Style (16th Edition):

Kryshchenko (Chubatiuk), Alona. “Nonparametric estimation of an unknown probability distribution using maximum likelihood and Bayesian approaches.” 2013. Doctoral Dissertation, University of Southern California. Accessed October 19, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/309797/rec/4448.

MLA Handbook (7th Edition):

Kryshchenko (Chubatiuk), Alona. “Nonparametric estimation of an unknown probability distribution using maximum likelihood and Bayesian approaches.” 2013. Web. 19 Oct 2019.

Vancouver:

Kryshchenko (Chubatiuk) A. Nonparametric estimation of an unknown probability distribution using maximum likelihood and Bayesian approaches. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2019 Oct 19]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/309797/rec/4448.

Council of Science Editors:

Kryshchenko (Chubatiuk) A. Nonparametric estimation of an unknown probability distribution using maximum likelihood and Bayesian approaches. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/309797/rec/4448


Iowa State University

10. Guo, Jiqiang. Bayesian methods for system reliability and community detection.

Degree: 2011, Iowa State University

Bayesian methods are valuable for their natural incorporation of prior information and their practical convenience for modeling and estimation. This dissertation develops flexible Bayesian parametric… (more)

Subjects/Keywords: Bayesian; Bayesian nonparametric; Community detection; Complex system; Network; System reliability; Statistics and Probability

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APA (6th Edition):

Guo, J. (2011). Bayesian methods for system reliability and community detection. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/12240

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Guo, Jiqiang. “Bayesian methods for system reliability and community detection.” 2011. Thesis, Iowa State University. Accessed October 19, 2019. https://lib.dr.iastate.edu/etd/12240.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Guo, Jiqiang. “Bayesian methods for system reliability and community detection.” 2011. Web. 19 Oct 2019.

Vancouver:

Guo J. Bayesian methods for system reliability and community detection. [Internet] [Thesis]. Iowa State University; 2011. [cited 2019 Oct 19]. Available from: https://lib.dr.iastate.edu/etd/12240.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Guo J. Bayesian methods for system reliability and community detection. [Thesis]. Iowa State University; 2011. Available from: https://lib.dr.iastate.edu/etd/12240

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Toronto

11. Deshwar, Amit Gulab. Tumor Gene Expression Purification Using Infinite Mixture Topic Models.

Degree: 2013, University of Toronto

There is significant interest in using gene expression measurements to aid in the personalization of medical treatment. The presence of significant normal tissue contamination in… (more)

Subjects/Keywords: Bayesian methods; Gene expression purficiation; Bayesian Nonparametric; Topic models; 0984; 0800; 0544

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APA (6th Edition):

Deshwar, A. G. (2013). Tumor Gene Expression Purification Using Infinite Mixture Topic Models. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/35597

Chicago Manual of Style (16th Edition):

Deshwar, Amit Gulab. “Tumor Gene Expression Purification Using Infinite Mixture Topic Models.” 2013. Masters Thesis, University of Toronto. Accessed October 19, 2019. http://hdl.handle.net/1807/35597.

MLA Handbook (7th Edition):

Deshwar, Amit Gulab. “Tumor Gene Expression Purification Using Infinite Mixture Topic Models.” 2013. Web. 19 Oct 2019.

Vancouver:

Deshwar AG. Tumor Gene Expression Purification Using Infinite Mixture Topic Models. [Internet] [Masters thesis]. University of Toronto; 2013. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1807/35597.

Council of Science Editors:

Deshwar AG. Tumor Gene Expression Purification Using Infinite Mixture Topic Models. [Masters Thesis]. University of Toronto; 2013. Available from: http://hdl.handle.net/1807/35597


Texas A&M University

12. Ray, Shubhankar. Nonparametric Bayesian analysis of some clustering problems.

Degree: 2006, Texas A&M University

Nonparametric Bayesian models have been researched extensively in the past 10 years following the work of Escobar and West (1995) on sampling schemes for Dirichlet… (more)

Subjects/Keywords: Nonparametric Bayesian; Clustering; Dirichlet Processes

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APA (6th Edition):

Ray, S. (2006). Nonparametric Bayesian analysis of some clustering problems. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/4251

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ray, Shubhankar. “Nonparametric Bayesian analysis of some clustering problems.” 2006. Thesis, Texas A&M University. Accessed October 19, 2019. http://hdl.handle.net/1969.1/4251.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ray, Shubhankar. “Nonparametric Bayesian analysis of some clustering problems.” 2006. Web. 19 Oct 2019.

Vancouver:

Ray S. Nonparametric Bayesian analysis of some clustering problems. [Internet] [Thesis]. Texas A&M University; 2006. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1969.1/4251.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ray S. Nonparametric Bayesian analysis of some clustering problems. [Thesis]. Texas A&M University; 2006. Available from: http://hdl.handle.net/1969.1/4251

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


The Ohio State University

13. Xu, Zhiguang. Modeling Non-Gaussian Time-correlated Data Using Nonparametric Bayesian Method.

Degree: PhD, Statistics, 2014, The Ohio State University

 This dissertation proposes nonparametric Bayesian methods to study a large class of non-Gaussian time-correlated data, including non-Gaussian time series and non-Gaussian longitudinal datasets.When a time… (more)

Subjects/Keywords: Statistics; time series, longitudinal data, copula, nonparametric Bayesian method

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APA (6th Edition):

Xu, Z. (2014). Modeling Non-Gaussian Time-correlated Data Using Nonparametric Bayesian Method. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1406068732

Chicago Manual of Style (16th Edition):

Xu, Zhiguang. “Modeling Non-Gaussian Time-correlated Data Using Nonparametric Bayesian Method.” 2014. Doctoral Dissertation, The Ohio State University. Accessed October 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406068732.

MLA Handbook (7th Edition):

Xu, Zhiguang. “Modeling Non-Gaussian Time-correlated Data Using Nonparametric Bayesian Method.” 2014. Web. 19 Oct 2019.

Vancouver:

Xu Z. Modeling Non-Gaussian Time-correlated Data Using Nonparametric Bayesian Method. [Internet] [Doctoral dissertation]. The Ohio State University; 2014. [cited 2019 Oct 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406068732.

Council of Science Editors:

Xu Z. Modeling Non-Gaussian Time-correlated Data Using Nonparametric Bayesian Method. [Doctoral Dissertation]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406068732


University of California – Berkeley

14. Huoh, Yu-Jay. Sensitivity Analysis of Stochastic Simulators with Information Theory.

Degree: Statistics, 2013, University of California – Berkeley

 The increased computational power available today has made the use of computer models or simulators common in many fields. While there is a widely adopted… (more)

Subjects/Keywords: Statistics; Bayesian nonparametric density regression; information theory; sensitivity analysis; uncertainty quantification

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APA (6th Edition):

Huoh, Y. (2013). Sensitivity Analysis of Stochastic Simulators with Information Theory. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/7rt519fd

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Huoh, Yu-Jay. “Sensitivity Analysis of Stochastic Simulators with Information Theory.” 2013. Thesis, University of California – Berkeley. Accessed October 19, 2019. http://www.escholarship.org/uc/item/7rt519fd.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Huoh, Yu-Jay. “Sensitivity Analysis of Stochastic Simulators with Information Theory.” 2013. Web. 19 Oct 2019.

Vancouver:

Huoh Y. Sensitivity Analysis of Stochastic Simulators with Information Theory. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2019 Oct 19]. Available from: http://www.escholarship.org/uc/item/7rt519fd.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huoh Y. Sensitivity Analysis of Stochastic Simulators with Information Theory. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/7rt519fd

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Kansas State University

15. Zhuang, Zhihe. Modeling and projection of respondent driven network samples.

Degree: MS, Department of Statistics, 2018, Kansas State University

 The term network has become part of our everyday vocabulary. The more popular are perhaps the social ones, but the concept also includes business partnerships,… (more)

Subjects/Keywords: Networks; Respondent Driven Sampling; Nonparametric Bayesian; Sampling Methods; Stochastic Blockmodel

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APA (6th Edition):

Zhuang, Z. (2018). Modeling and projection of respondent driven network samples. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/39157

Chicago Manual of Style (16th Edition):

Zhuang, Zhihe. “Modeling and projection of respondent driven network samples.” 2018. Masters Thesis, Kansas State University. Accessed October 19, 2019. http://hdl.handle.net/2097/39157.

MLA Handbook (7th Edition):

Zhuang, Zhihe. “Modeling and projection of respondent driven network samples.” 2018. Web. 19 Oct 2019.

Vancouver:

Zhuang Z. Modeling and projection of respondent driven network samples. [Internet] [Masters thesis]. Kansas State University; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2097/39157.

Council of Science Editors:

Zhuang Z. Modeling and projection of respondent driven network samples. [Masters Thesis]. Kansas State University; 2018. Available from: http://hdl.handle.net/2097/39157


University of California – Berkeley

16. Miller, Kurt Tadayuki. Bayesian Nonparametric Latent Feature Models.

Degree: Electrical Engineering & Computer Sciences, 2011, University of California – Berkeley

 Priors for Bayesian nonparametric latent feature models were originally developed a little over five years ago, sparking interest in a new type of Bayesian nonparametric(more)

Subjects/Keywords: Computer science; Statistics; Bayesian; Feature; Latent; Model; Nonparametric

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APA (6th Edition):

Miller, K. T. (2011). Bayesian Nonparametric Latent Feature Models. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9z0568hd

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Miller, Kurt Tadayuki. “Bayesian Nonparametric Latent Feature Models.” 2011. Thesis, University of California – Berkeley. Accessed October 19, 2019. http://www.escholarship.org/uc/item/9z0568hd.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Miller, Kurt Tadayuki. “Bayesian Nonparametric Latent Feature Models.” 2011. Web. 19 Oct 2019.

Vancouver:

Miller KT. Bayesian Nonparametric Latent Feature Models. [Internet] [Thesis]. University of California – Berkeley; 2011. [cited 2019 Oct 19]. Available from: http://www.escholarship.org/uc/item/9z0568hd.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Miller KT. Bayesian Nonparametric Latent Feature Models. [Thesis]. University of California – Berkeley; 2011. Available from: http://www.escholarship.org/uc/item/9z0568hd

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

17. Lindon, Michael Scott. Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection .

Degree: 2018, Duke University

  This thesis naturally divides itself into two sections. The first two chapters concern the development of Bayesian semi-parametric models for arrival times. Chapter 2… (more)

Subjects/Keywords: Statistics; Bayesian; Mixed Integer; Nonparametric; Optimization; Poisson Process; Variable Selection

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

APA (6th Edition):

Lindon, M. S. (2018). Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16858

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lindon, Michael Scott. “Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection .” 2018. Thesis, Duke University. Accessed October 19, 2019. http://hdl.handle.net/10161/16858.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lindon, Michael Scott. “Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection .” 2018. Web. 19 Oct 2019.

Vancouver:

Lindon MS. Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10161/16858.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lindon MS. Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/16858

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

18. Nguyen, Nghi Le Phuong. Essays on Propensity Score Methods for Causal Inference in Observational Studies .

Degree: 2018, Duke University

  In this dissertation, I present three essays from three different research projects and they involve different usages of propensity scores in drawing causal inferences… (more)

Subjects/Keywords: Statistics; bayesian nonparametric; causal inference; multilevel; propensity scores; sensitivity analysis; unconfoundedness

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APA (6th Edition):

Nguyen, N. L. P. (2018). Essays on Propensity Score Methods for Causal Inference in Observational Studies . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/17521

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Nguyen, Nghi Le Phuong. “Essays on Propensity Score Methods for Causal Inference in Observational Studies .” 2018. Thesis, Duke University. Accessed October 19, 2019. http://hdl.handle.net/10161/17521.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Nguyen, Nghi Le Phuong. “Essays on Propensity Score Methods for Causal Inference in Observational Studies .” 2018. Web. 19 Oct 2019.

Vancouver:

Nguyen NLP. Essays on Propensity Score Methods for Causal Inference in Observational Studies . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10161/17521.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nguyen NLP. Essays on Propensity Score Methods for Causal Inference in Observational Studies . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/17521

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

19. Wei, Hongchuan. Sensor Planning for Bayesian Nonparametric Target Modeling .

Degree: 2016, Duke University

Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including… (more)

Subjects/Keywords: Mechanical engineering; Bayesian nonparametric; Dirichlet process; Gaussian process; sensor planning

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APA (6th Edition):

Wei, H. (2016). Sensor Planning for Bayesian Nonparametric Target Modeling . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/12863

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wei, Hongchuan. “Sensor Planning for Bayesian Nonparametric Target Modeling .” 2016. Thesis, Duke University. Accessed October 19, 2019. http://hdl.handle.net/10161/12863.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wei, Hongchuan. “Sensor Planning for Bayesian Nonparametric Target Modeling .” 2016. Web. 19 Oct 2019.

Vancouver:

Wei H. Sensor Planning for Bayesian Nonparametric Target Modeling . [Internet] [Thesis]. Duke University; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10161/12863.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wei H. Sensor Planning for Bayesian Nonparametric Target Modeling . [Thesis]. Duke University; 2016. Available from: http://hdl.handle.net/10161/12863

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

20. Morton, Kenneth D. Bayesian Techniques for Adaptive Acoustic Surveillance .

Degree: 2010, Duke University

  Automated acoustic sensing systems are required to detect, classify and localize acoustic signals in real-time. Despite the fact that humans are capable of performing… (more)

Subjects/Keywords: Engineering, Electronics and Electrical; Acoustic; Autoregressive; Bayesian; Nonparametric

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APA (6th Edition):

Morton, K. D. (2010). Bayesian Techniques for Adaptive Acoustic Surveillance . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/2477

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Morton, Kenneth D. “Bayesian Techniques for Adaptive Acoustic Surveillance .” 2010. Thesis, Duke University. Accessed October 19, 2019. http://hdl.handle.net/10161/2477.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Morton, Kenneth D. “Bayesian Techniques for Adaptive Acoustic Surveillance .” 2010. Web. 19 Oct 2019.

Vancouver:

Morton KD. Bayesian Techniques for Adaptive Acoustic Surveillance . [Internet] [Thesis]. Duke University; 2010. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10161/2477.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Morton KD. Bayesian Techniques for Adaptive Acoustic Surveillance . [Thesis]. Duke University; 2010. Available from: http://hdl.handle.net/10161/2477

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cambridge

21. Ray, Kolyan Michael. Asymptotic theory for Bayesian nonparametric procedures in inverse problems.

Degree: PhD, 2015, University of Cambridge

 The main goal of this thesis is to investigate the frequentist asymptotic properties of nonparametric Bayesian procedures in inverse problems and the Gaussian white noise… (more)

Subjects/Keywords: 510; Bayesian nonparametric statistics; Inverse problems; Adaptation; Uncertainty quantification

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

APA (6th Edition):

Ray, K. M. (2015). Asymptotic theory for Bayesian nonparametric procedures in inverse problems. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/278387 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708537

Chicago Manual of Style (16th Edition):

Ray, Kolyan Michael. “Asymptotic theory for Bayesian nonparametric procedures in inverse problems.” 2015. Doctoral Dissertation, University of Cambridge. Accessed October 19, 2019. https://www.repository.cam.ac.uk/handle/1810/278387 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708537.

MLA Handbook (7th Edition):

Ray, Kolyan Michael. “Asymptotic theory for Bayesian nonparametric procedures in inverse problems.” 2015. Web. 19 Oct 2019.

Vancouver:

Ray KM. Asymptotic theory for Bayesian nonparametric procedures in inverse problems. [Internet] [Doctoral dissertation]. University of Cambridge; 2015. [cited 2019 Oct 19]. Available from: https://www.repository.cam.ac.uk/handle/1810/278387 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708537.

Council of Science Editors:

Ray KM. Asymptotic theory for Bayesian nonparametric procedures in inverse problems. [Doctoral Dissertation]. University of Cambridge; 2015. Available from: https://www.repository.cam.ac.uk/handle/1810/278387 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708537


University of Ottawa

22. Han, Xuejun. On Nonparametric Bayesian Inference for Tukey Depth .

Degree: 2017, University of Ottawa

 The Dirichlet process is perhaps the most popular prior used in the nonparametric Bayesian inference. This prior which is placed on the space of probability… (more)

Subjects/Keywords: Nonparametric Bayesian Inference; Dirichlet Process; Data Depth; Tukey Depth

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APA (6th Edition):

Han, X. (2017). On Nonparametric Bayesian Inference for Tukey Depth . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/36533

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Han, Xuejun. “On Nonparametric Bayesian Inference for Tukey Depth .” 2017. Thesis, University of Ottawa. Accessed October 19, 2019. http://hdl.handle.net/10393/36533.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Han, Xuejun. “On Nonparametric Bayesian Inference for Tukey Depth .” 2017. Web. 19 Oct 2019.

Vancouver:

Han X. On Nonparametric Bayesian Inference for Tukey Depth . [Internet] [Thesis]. University of Ottawa; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10393/36533.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Han X. On Nonparametric Bayesian Inference for Tukey Depth . [Thesis]. University of Ottawa; 2017. Available from: http://hdl.handle.net/10393/36533

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

23. Wang, Xu. Searching, Clustering and Regression on non-Euclidean Spaces.

Degree: PhD, Mathematics, 2015, University of Minnesota

 This dissertation considers three common tasks (e.g., searching, clustering, regression) over Riemannian spaces. The first task considers the problem of efficiently deciding which of a… (more)

Subjects/Keywords: Bayesian; Clustering; Fast search; Manifold; Nonparametric regression; Spectral clustering

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

APA (6th Edition):

Wang, X. (2015). Searching, Clustering and Regression on non-Euclidean Spaces. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/175554

Chicago Manual of Style (16th Edition):

Wang, Xu. “Searching, Clustering and Regression on non-Euclidean Spaces.” 2015. Doctoral Dissertation, University of Minnesota. Accessed October 19, 2019. http://hdl.handle.net/11299/175554.

MLA Handbook (7th Edition):

Wang, Xu. “Searching, Clustering and Regression on non-Euclidean Spaces.” 2015. Web. 19 Oct 2019.

Vancouver:

Wang X. Searching, Clustering and Regression on non-Euclidean Spaces. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/11299/175554.

Council of Science Editors:

Wang X. Searching, Clustering and Regression on non-Euclidean Spaces. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/175554


University of Minnesota

24. Arbet, Jaron. Robust Variance Component Models and Powerful Variable Selection Methods for Addressing Missing Heritability.

Degree: PhD, Biostatistics, 2018, University of Minnesota

 The development of a complex human disease is an intricate interplay of genetic and environmental factors. Broadly speaking, “heritability” is defined as the proportion of… (more)

Subjects/Keywords: Bayesian; Gaussian Process; heritability; Nonparametric; twin studies; variable selection

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APA (6th Edition):

Arbet, J. (2018). Robust Variance Component Models and Powerful Variable Selection Methods for Addressing Missing Heritability. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/201084

Chicago Manual of Style (16th Edition):

Arbet, Jaron. “Robust Variance Component Models and Powerful Variable Selection Methods for Addressing Missing Heritability.” 2018. Doctoral Dissertation, University of Minnesota. Accessed October 19, 2019. http://hdl.handle.net/11299/201084.

MLA Handbook (7th Edition):

Arbet, Jaron. “Robust Variance Component Models and Powerful Variable Selection Methods for Addressing Missing Heritability.” 2018. Web. 19 Oct 2019.

Vancouver:

Arbet J. Robust Variance Component Models and Powerful Variable Selection Methods for Addressing Missing Heritability. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/11299/201084.

Council of Science Editors:

Arbet J. Robust Variance Component Models and Powerful Variable Selection Methods for Addressing Missing Heritability. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/201084


University of New South Wales

25. Wang, Yi. Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness.

Degree: Computer Science & Engineering, 2016, University of New South Wales

 In this thesis, we emphasise on three types of Bayesian nonparametric probabilisticclustering problem: the class-based clustering, the feature-based clustering and theco-clustering. The mapping relationship between… (more)

Subjects/Keywords: Approximate Inference; Bayesian Nonparametric; MCMC; Stochastic Block Model

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APA (6th Edition):

Wang, Y. (2016). Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Wang, Yi. “Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness.” 2016. Doctoral Dissertation, University of New South Wales. Accessed October 19, 2019. http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true.

MLA Handbook (7th Edition):

Wang, Yi. “Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness.” 2016. Web. 19 Oct 2019.

Vancouver:

Wang Y. Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Oct 19]. Available from: http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true.

Council of Science Editors:

Wang Y. Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true


University of Minnesota

26. Hart, Brian. Methods for Analyzing Multi-Subject Resting-State Neuroimaging Time Series Data.

Degree: PhD, Biostatistics, 2019, University of Minnesota

 Resting-state neuroimaging modalities such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) collect data in the form of time series which represent the activity… (more)

Subjects/Keywords: Bayesian Nonparametric; Multi-subject; Neuroimaging; Resting-State; Time Series

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APA (6th Edition):

Hart, B. (2019). Methods for Analyzing Multi-Subject Resting-State Neuroimaging Time Series Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206265

Chicago Manual of Style (16th Edition):

Hart, Brian. “Methods for Analyzing Multi-Subject Resting-State Neuroimaging Time Series Data.” 2019. Doctoral Dissertation, University of Minnesota. Accessed October 19, 2019. http://hdl.handle.net/11299/206265.

MLA Handbook (7th Edition):

Hart, Brian. “Methods for Analyzing Multi-Subject Resting-State Neuroimaging Time Series Data.” 2019. Web. 19 Oct 2019.

Vancouver:

Hart B. Methods for Analyzing Multi-Subject Resting-State Neuroimaging Time Series Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/11299/206265.

Council of Science Editors:

Hart B. Methods for Analyzing Multi-Subject Resting-State Neuroimaging Time Series Data. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206265


University of Texas – Austin

27. Schaefer, Kayla Hope. Document clustering with nonparametric hierarchical topic modeling.

Degree: MSin Statistics, Statistics, 2015, University of Texas – Austin

 Since its introduction, topic modeling has been a fundamental tool in analyzing corpus structures. While the Relational Topic Model provides a way to link, and… (more)

Subjects/Keywords: Clustering; Nonparametric Bayesian statistics; Hierarchical models; Gibbs sampling; Shakespeare

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APA (6th Edition):

Schaefer, K. H. (2015). Document clustering with nonparametric hierarchical topic modeling. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32498

Chicago Manual of Style (16th Edition):

Schaefer, Kayla Hope. “Document clustering with nonparametric hierarchical topic modeling.” 2015. Masters Thesis, University of Texas – Austin. Accessed October 19, 2019. http://hdl.handle.net/2152/32498.

MLA Handbook (7th Edition):

Schaefer, Kayla Hope. “Document clustering with nonparametric hierarchical topic modeling.” 2015. Web. 19 Oct 2019.

Vancouver:

Schaefer KH. Document clustering with nonparametric hierarchical topic modeling. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2152/32498.

Council of Science Editors:

Schaefer KH. Document clustering with nonparametric hierarchical topic modeling. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32498


University of Texas – Austin

28. Anderson, Dylan Zachary. Supervised gamma process Poisson factorization.

Degree: MSin Engineering, Electrical and Computer Engineering, 2015, University of Texas – Austin

 This thesis develops the supervised gamma process Poisson factorization (S-GPPF) framework, a novel supervised topic model for joint modeling of count matrices and document labels.… (more)

Subjects/Keywords: Supervised topic modeling; Bayesian nonparametric; Gamma process; Poisson factorization

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APA (6th Edition):

Anderson, D. Z. (2015). Supervised gamma process Poisson factorization. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31866

Chicago Manual of Style (16th Edition):

Anderson, Dylan Zachary. “Supervised gamma process Poisson factorization.” 2015. Masters Thesis, University of Texas – Austin. Accessed October 19, 2019. http://hdl.handle.net/2152/31866.

MLA Handbook (7th Edition):

Anderson, Dylan Zachary. “Supervised gamma process Poisson factorization.” 2015. Web. 19 Oct 2019.

Vancouver:

Anderson DZ. Supervised gamma process Poisson factorization. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2152/31866.

Council of Science Editors:

Anderson DZ. Supervised gamma process Poisson factorization. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31866


Australian National University

29. Lim, Kar Wai. Nonparametric Bayesian Topic Modelling with Auxiliary Data .

Degree: 2016, Australian National University

 The intent of this dissertation in computer science is to study topic models for text analytics. The first objective of this dissertation is to incorporate… (more)

Subjects/Keywords: Bayesian nonparametric; topic modelling; hierarchical Pitman-Yor process

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

APA (6th Edition):

Lim, K. W. (2016). Nonparametric Bayesian Topic Modelling with Auxiliary Data . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/107151

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lim, Kar Wai. “Nonparametric Bayesian Topic Modelling with Auxiliary Data .” 2016. Thesis, Australian National University. Accessed October 19, 2019. http://hdl.handle.net/1885/107151.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lim, Kar Wai. “Nonparametric Bayesian Topic Modelling with Auxiliary Data .” 2016. Web. 19 Oct 2019.

Vancouver:

Lim KW. Nonparametric Bayesian Topic Modelling with Auxiliary Data . [Internet] [Thesis]. Australian National University; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1885/107151.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lim KW. Nonparametric Bayesian Topic Modelling with Auxiliary Data . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/107151

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Hong Kong University of Science and Technology

30. Ho, Man Wai. Bayesian inference for models with monotone densities and hazard rates.

Degree: 2002, Hong Kong University of Science and Technology

 This thesis discusses nonparametric estimations in models with decreasing densities from a Bayesian viewpoint. The decreasing density is modeled as a scale mixture of uniforms.… (more)

Subjects/Keywords: Bayesian statistical decision theory; Nonparametric statistics

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APA (6th Edition):

Ho, M. W. (2002). Bayesian inference for models with monotone densities and hazard rates. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b773902 ; http://repository.ust.hk/ir/bitstream/1783.1-602/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ho, Man Wai. “Bayesian inference for models with monotone densities and hazard rates.” 2002. Thesis, Hong Kong University of Science and Technology. Accessed October 19, 2019. https://doi.org/10.14711/thesis-b773902 ; http://repository.ust.hk/ir/bitstream/1783.1-602/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ho, Man Wai. “Bayesian inference for models with monotone densities and hazard rates.” 2002. Web. 19 Oct 2019.

Vancouver:

Ho MW. Bayesian inference for models with monotone densities and hazard rates. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2002. [cited 2019 Oct 19]. Available from: https://doi.org/10.14711/thesis-b773902 ; http://repository.ust.hk/ir/bitstream/1783.1-602/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ho MW. Bayesian inference for models with monotone densities and hazard rates. [Thesis]. Hong Kong University of Science and Technology; 2002. Available from: https://doi.org/10.14711/thesis-b773902 ; http://repository.ust.hk/ir/bitstream/1783.1-602/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

[1] [2] [3] [4]

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