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108 total matches.

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- 2010 – 2014 (47)
- 2005 – 2009 (12)

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- Docteur es (11)

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University of Georgia

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

Degree: PhD, Statistics, 2004, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/yang_ying_200412_phd

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1969.1/166096

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/10161/16370

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

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

URL: http://hdl.handle.net/10919/28398

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11375/16575

►

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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://handle.unsw.edu.au/1959.4/58764 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47509/SOURCE02?view=true

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://trepo.tuni.fi/handle/10024/104066

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/10161/8065

► 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 (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

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

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/309797/rec/4448

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://lib.dr.iastate.edu/etd/12240

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/1807/35597

►

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1969.1/4251

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1406068732

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://www.escholarship.org/uc/item/7rt519fd

► 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 (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/2097/39157

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://www.escholarship.org/uc/item/9z0568hd

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/10161/16858

► 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 (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/10161/17521

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

Duke University

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

Degree: 2016, Duke University

URL: http://hdl.handle.net/10161/12863

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

Duke University

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

Degree: 2010, Duke University

URL: http://hdl.handle.net/10161/2477

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://www.repository.cam.ac.uk/handle/1810/278387 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708537

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/10393/36533

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/11299/175554

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11299/201084

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11299/206265

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2152/32498

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2152/31866

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1885/107151

► 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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://doi.org/10.14711/thesis-b773902 ; http://repository.ust.hk/ir/bitstream/1783.1-602/1/th_redirect.html

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation