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You searched for +publisher:"University of Colorado" +contributor:("Jem N. Corcoran"). Showing records 1 – 9 of 9 total matches.

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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

  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

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

APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

  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

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

APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

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