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You searched for `+publisher:"University of Colorado" +contributor:("Jem N. Corcoran")`

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University of Colorado

1. Goetz-Weiss, Lukas Ruediger Nelson. Dimensionality Detection and the Geometric Median on Data Manifolds.

Degree: MS, Applied Mathematics, 2017, University of Colorado

URL: https://scholar.colorado.edu/appm_gradetds/95

► In many applications high-dimensional observations are assumed to arrange on or near a low-dimensional manifold embedded in an ambient Euclidean space. In this thesis,…
(more)

Subjects/Keywords: Dimensionality Detection; Equation-Free; Geometric Median; High-Dimensional Processes; Manifold Learning; Applied Mechanics

Record Details Similar Records

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

APA (6^{th} Edition):

Goetz-Weiss, L. R. N. (2017). Dimensionality Detection and the Geometric Median on Data Manifolds. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/95

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

Goetz-Weiss, Lukas Ruediger Nelson. “Dimensionality Detection and the Geometric Median on Data Manifolds.” 2017. Masters Thesis, University of Colorado. Accessed December 13, 2019. https://scholar.colorado.edu/appm_gradetds/95.

MLA Handbook (7^{th} Edition):

Goetz-Weiss, Lukas Ruediger Nelson. “Dimensionality Detection and the Geometric Median on Data Manifolds.” 2017. Web. 13 Dec 2019.

Vancouver:

Goetz-Weiss LRN. Dimensionality Detection and the Geometric Median on Data Manifolds. [Internet] [Masters thesis]. University of Colorado; 2017. [cited 2019 Dec 13]. Available from: https://scholar.colorado.edu/appm_gradetds/95.

Council of Science Editors:

Goetz-Weiss LRN. Dimensionality Detection and the Geometric Median on Data Manifolds. [Masters Thesis]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/appm_gradetds/95

University of Colorado

2. Levine, Nicholas D. Using Minimum Description Length for Discretization Classification of Data Modeled by Bayesian Networks.

Degree: MS, Applied Mathematics, 2011, University of Colorado

URL: http://scholar.colorado.edu/appm_gradetds/21

► Bayesian networks are a graphical models that encode conditional probability relationships among multiple random variables. Able to model many variables at once, their applications…
(more)

Subjects/Keywords: Applied Mathematics

Record Details Similar Records

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

APA (6^{th} Edition):

Levine, N. D. (2011). Using Minimum Description Length for Discretization Classification of Data Modeled by Bayesian Networks. (Masters Thesis). University of Colorado. Retrieved from http://scholar.colorado.edu/appm_gradetds/21

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

Levine, Nicholas D. “Using Minimum Description Length for Discretization Classification of Data Modeled by Bayesian Networks.” 2011. Masters Thesis, University of Colorado. Accessed December 13, 2019. http://scholar.colorado.edu/appm_gradetds/21.

MLA Handbook (7^{th} Edition):

Levine, Nicholas D. “Using Minimum Description Length for Discretization Classification of Data Modeled by Bayesian Networks.” 2011. Web. 13 Dec 2019.

Vancouver:

Levine ND. Using Minimum Description Length for Discretization Classification of Data Modeled by Bayesian Networks. [Internet] [Masters thesis]. University of Colorado; 2011. [cited 2019 Dec 13]. Available from: http://scholar.colorado.edu/appm_gradetds/21.

Council of Science Editors:

Levine ND. Using Minimum Description Length for Discretization Classification of Data Modeled by Bayesian Networks. [Masters Thesis]. University of Colorado; 2011. Available from: http://scholar.colorado.edu/appm_gradetds/21

University of Colorado

3. Tran, Dai Daniel. An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length.

Degree: MS, Applied Mathematics, 2013, University of Colorado

URL: http://scholar.colorado.edu/appm_gradetds/41

► Bayesian networks are widely considered as powerful tools for modeling risk assessment, uncertainty, and decision making. They have been extensively employed to develop decision…
(more)

Subjects/Keywords: Bayesian Network; Minimum Description Length; Probabilities; Risk analysis; Applied Mathematics; Statistics and Probability

Record Details Similar Records

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

APA (6^{th} Edition):

Tran, D. D. (2013). An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length. (Masters Thesis). University of Colorado. Retrieved from http://scholar.colorado.edu/appm_gradetds/41

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

Tran, Dai Daniel. “An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length.” 2013. Masters Thesis, University of Colorado. Accessed December 13, 2019. http://scholar.colorado.edu/appm_gradetds/41.

MLA Handbook (7^{th} Edition):

Tran, Dai Daniel. “An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length.” 2013. Web. 13 Dec 2019.

Vancouver:

Tran DD. An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length. [Internet] [Masters thesis]. University of Colorado; 2013. [cited 2019 Dec 13]. Available from: http://scholar.colorado.edu/appm_gradetds/41.

Council of Science Editors:

Tran DD. An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length. [Masters Thesis]. University of Colorado; 2013. Available from: http://scholar.colorado.edu/appm_gradetds/41

University of Colorado

4. Klein, Dylan Lowell. Efficient Particle Tracking Algorithms for Solute Transport in Fracture Rock with Absorption and Matrix Diffusion.

Degree: MS, Applied Mathematics, 2013, University of Colorado

URL: http://scholar.colorado.edu/appm_gradetds/45

► In this paper, we study solute transport in an individual fracture and the surrounding porous rock. Specifically, we consider a parallel-plate model of a…
(more)

Subjects/Keywords: Absorption; Fracture; Modeling; Particle; Solute; Transport; Applied Mathematics; Geology; Water Resource Management

Record Details Similar Records

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

APA (6^{th} Edition):

Klein, D. L. (2013). Efficient Particle Tracking Algorithms for Solute Transport in Fracture Rock with Absorption and Matrix Diffusion. (Masters Thesis). University of Colorado. Retrieved from http://scholar.colorado.edu/appm_gradetds/45

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

Klein, Dylan Lowell. “Efficient Particle Tracking Algorithms for Solute Transport in Fracture Rock with Absorption and Matrix Diffusion.” 2013. Masters Thesis, University of Colorado. Accessed December 13, 2019. http://scholar.colorado.edu/appm_gradetds/45.

MLA Handbook (7^{th} Edition):

Klein, Dylan Lowell. “Efficient Particle Tracking Algorithms for Solute Transport in Fracture Rock with Absorption and Matrix Diffusion.” 2013. Web. 13 Dec 2019.

Vancouver:

Klein DL. Efficient Particle Tracking Algorithms for Solute Transport in Fracture Rock with Absorption and Matrix Diffusion. [Internet] [Masters thesis]. University of Colorado; 2013. [cited 2019 Dec 13]. Available from: http://scholar.colorado.edu/appm_gradetds/45.

Council of Science Editors:

Klein DL. Efficient Particle Tracking Algorithms for Solute Transport in Fracture Rock with Absorption and Matrix Diffusion. [Masters Thesis]. University of Colorado; 2013. Available from: http://scholar.colorado.edu/appm_gradetds/45

University of Colorado

5. Sidrow, Evan Jeffrey. Network Structure Sampling in Bayesian Networks via Perfect Sampling from Linear Extensions.

Degree: MS, 2018, University of Colorado

URL: https://scholar.colorado.edu/appm_gradetds/110

► Bayesian networks are widely considered as powerful tools for modeling risk assessment, uncertainty, and decision making. They have been extensively employed to develop decision…
(more)

Subjects/Keywords: bayesian networks; directed acyclic graphs; linear extensions; partial ordering; perfect sampling; Applied Mathematics; Computer Sciences; Statistics and Probability

Record Details Similar Records

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

APA (6^{th} Edition):

Sidrow, E. J. (2018). Network Structure Sampling in Bayesian Networks via Perfect Sampling from Linear Extensions. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/110

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

Sidrow, Evan Jeffrey. “Network Structure Sampling in Bayesian Networks via Perfect Sampling from Linear Extensions.” 2018. Masters Thesis, University of Colorado. Accessed December 13, 2019. https://scholar.colorado.edu/appm_gradetds/110.

MLA Handbook (7^{th} Edition):

Sidrow, Evan Jeffrey. “Network Structure Sampling in Bayesian Networks via Perfect Sampling from Linear Extensions.” 2018. Web. 13 Dec 2019.

Vancouver:

Sidrow EJ. Network Structure Sampling in Bayesian Networks via Perfect Sampling from Linear Extensions. [Internet] [Masters thesis]. University of Colorado; 2018. [cited 2019 Dec 13]. Available from: https://scholar.colorado.edu/appm_gradetds/110.

Council of Science Editors:

Sidrow EJ. Network Structure Sampling in Bayesian Networks via Perfect Sampling from Linear Extensions. [Masters Thesis]. University of Colorado; 2018. Available from: https://scholar.colorado.edu/appm_gradetds/110

University of Colorado

6. Goetz-Weiss, Lukas Ruediger Nelson. Dimensionality Detection and the Geometric Median on Data Manifolds.

Degree: MS, 2017, University of Colorado

URL: https://scholar.colorado.edu/appm_gradetds/131

► In many applications high-dimensional observations are assumed to arrange on or near a low-dimensional manifold embedded in an ambient Euclidean space. In this thesis,…
(more)

Subjects/Keywords: dimensionality detection; equation-free; geometric median; high-dimensional processes; manifold learning; Applied Mathematics; Geometry and Topology

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Goetz-Weiss, L. R. N. (2017). Dimensionality Detection and the Geometric Median on Data Manifolds. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/131

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

Goetz-Weiss, Lukas Ruediger Nelson. “Dimensionality Detection and the Geometric Median on Data Manifolds.” 2017. Masters Thesis, University of Colorado. Accessed December 13, 2019. https://scholar.colorado.edu/appm_gradetds/131.

MLA Handbook (7^{th} Edition):

Goetz-Weiss, Lukas Ruediger Nelson. “Dimensionality Detection and the Geometric Median on Data Manifolds.” 2017. Web. 13 Dec 2019.

Vancouver:

Goetz-Weiss LRN. Dimensionality Detection and the Geometric Median on Data Manifolds. [Internet] [Masters thesis]. University of Colorado; 2017. [cited 2019 Dec 13]. Available from: https://scholar.colorado.edu/appm_gradetds/131.

Council of Science Editors:

Goetz-Weiss LRN. Dimensionality Detection and the Geometric Median on Data Manifolds. [Masters Thesis]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/appm_gradetds/131

University of Colorado

7. Romero, Henry Paul. Fundamental Limits of Network Communication with General Message Sets: A Combinatorial Approach.

Degree: PhD, Applied Mathematics, 2014, University of Colorado

URL: http://scholar.colorado.edu/appm_gradetds/61

► The classical theoretical framework for communication networks is based on the simplifying assumption that each message to be sent is known to a single…
(more)

Subjects/Keywords: Information Theory; DM Multiple Access Channel; MIMO Multiple Access Channel; Broadcast Channel; Inteference Channel; Computer Sciences; Electrical and Computer Engineering; Mathematics

Record Details Similar Records

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

APA (6^{th} Edition):

Romero, H. P. (2014). Fundamental Limits of Network Communication with General Message Sets: A Combinatorial Approach. (Doctoral Dissertation). University of Colorado. Retrieved from http://scholar.colorado.edu/appm_gradetds/61

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

Romero, Henry Paul. “Fundamental Limits of Network Communication with General Message Sets: A Combinatorial Approach.” 2014. Doctoral Dissertation, University of Colorado. Accessed December 13, 2019. http://scholar.colorado.edu/appm_gradetds/61.

MLA Handbook (7^{th} Edition):

Romero, Henry Paul. “Fundamental Limits of Network Communication with General Message Sets: A Combinatorial Approach.” 2014. Web. 13 Dec 2019.

Vancouver:

Romero HP. Fundamental Limits of Network Communication with General Message Sets: A Combinatorial Approach. [Internet] [Doctoral dissertation]. University of Colorado; 2014. [cited 2019 Dec 13]. Available from: http://scholar.colorado.edu/appm_gradetds/61.

Council of Science Editors:

Romero HP. Fundamental Limits of Network Communication with General Message Sets: A Combinatorial Approach. [Doctoral Dissertation]. University of Colorado; 2014. Available from: http://scholar.colorado.edu/appm_gradetds/61

University of Colorado

8. Monnig, Nathan D. From Nonlinear Embedding to Graph Distances: A Spectral Perspective.

Degree: PhD, Applied Mathematics, 2015, University of Colorado

URL: http://scholar.colorado.edu/appm_gradetds/64

► In this thesis, we explore applications of spectral graph theory to the analysis of complex datasets and networks. We consider spectral embeddings of general…
(more)

Subjects/Keywords: effective resistance; graph distances; graph theory; nonlinear dimension reduction; radial basis functions; spectral algorithms; Numerical Analysis and Computation; Set Theory

Record Details Similar Records

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

APA (6^{th} Edition):

Monnig, N. D. (2015). From Nonlinear Embedding to Graph Distances: A Spectral Perspective. (Doctoral Dissertation). University of Colorado. Retrieved from http://scholar.colorado.edu/appm_gradetds/64

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

Monnig, Nathan D. “From Nonlinear Embedding to Graph Distances: A Spectral Perspective.” 2015. Doctoral Dissertation, University of Colorado. Accessed December 13, 2019. http://scholar.colorado.edu/appm_gradetds/64.

MLA Handbook (7^{th} Edition):

Monnig, Nathan D. “From Nonlinear Embedding to Graph Distances: A Spectral Perspective.” 2015. Web. 13 Dec 2019.

Vancouver:

Monnig ND. From Nonlinear Embedding to Graph Distances: A Spectral Perspective. [Internet] [Doctoral dissertation]. University of Colorado; 2015. [cited 2019 Dec 13]. Available from: http://scholar.colorado.edu/appm_gradetds/64.

Council of Science Editors:

Monnig ND. From Nonlinear Embedding to Graph Distances: A Spectral Perspective. [Doctoral Dissertation]. University of Colorado; 2015. Available from: http://scholar.colorado.edu/appm_gradetds/64

University of Colorado

9. Jennings, Dale Kurtis. Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks.

Degree: PhD, Applied Mathematics, 2016, University of Colorado

URL: http://scholar.colorado.edu/appm_gradetds/81

► Markov Chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a desired probability distribution. While there exist many algorithms that attempt…
(more)

Subjects/Keywords: Applied Probability; Bayesian Networks; Birth and Death Process; Kac Model; MCMC; Particle Filtering; Applied Mathematics

Record Details Similar Records

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

APA (6^{th} Edition):

Jennings, D. K. (2016). Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks. (Doctoral Dissertation). University of Colorado. Retrieved from http://scholar.colorado.edu/appm_gradetds/81

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

Jennings, Dale Kurtis. “Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks.” 2016. Doctoral Dissertation, University of Colorado. Accessed December 13, 2019. http://scholar.colorado.edu/appm_gradetds/81.

MLA Handbook (7^{th} Edition):

Jennings, Dale Kurtis. “Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks.” 2016. Web. 13 Dec 2019.

Vancouver:

Jennings DK. Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks. [Internet] [Doctoral dissertation]. University of Colorado; 2016. [cited 2019 Dec 13]. Available from: http://scholar.colorado.edu/appm_gradetds/81.

Council of Science Editors:

Jennings DK. Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks. [Doctoral Dissertation]. University of Colorado; 2016. Available from: http://scholar.colorado.edu/appm_gradetds/81