Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Gaussian Process). Showing records 1 – 30 of 284 total matches.

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Search Limiters

Last 2 Years | English Only

Degrees

Levels

Languages

Country

▼ Search Limiters


University of Manchester

1. Ndiritu, Simon Wanjau. IMPUTING MISSING DATA FOR POLARIZATION MEASUREMENTS.

Degree: 2020, University of Manchester

 The presence of missing data in polarization measurements from radio telescopes negatively affects both the rotation measure (RM) transfer function and the Fara- day depth… (more)

Subjects/Keywords: Gaussian process modelling; Gaussian process regression; Imputing missing data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ndiritu, S. W. (2020). IMPUTING MISSING DATA FOR POLARIZATION MEASUREMENTS. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323502

Chicago Manual of Style (16th Edition):

Ndiritu, Simon Wanjau. “IMPUTING MISSING DATA FOR POLARIZATION MEASUREMENTS.” 2020. Doctoral Dissertation, University of Manchester. Accessed August 03, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323502.

MLA Handbook (7th Edition):

Ndiritu, Simon Wanjau. “IMPUTING MISSING DATA FOR POLARIZATION MEASUREMENTS.” 2020. Web. 03 Aug 2020.

Vancouver:

Ndiritu SW. IMPUTING MISSING DATA FOR POLARIZATION MEASUREMENTS. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2020 Aug 03]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323502.

Council of Science Editors:

Ndiritu SW. IMPUTING MISSING DATA FOR POLARIZATION MEASUREMENTS. [Doctoral Dissertation]. University of Manchester; 2020. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323502


Delft University of Technology

2. Krishnamoorthi, Sathish (author). Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression.

Degree: 2018, Delft University of Technology

 Industrial robots can be found in automotive, food, chemical, and electronics industries. These robots are often caged and are secluded from human beings. A recent… (more)

Subjects/Keywords: Semi-parametric Gaussian Process Regression; Serial Manipulators; Gaussian process regression

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Krishnamoorthi, S. (. (2018). Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9fd87374-ab6b-4574-a7f0-96dc27ff2eb0

Chicago Manual of Style (16th Edition):

Krishnamoorthi, Sathish (author). “Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression.” 2018. Masters Thesis, Delft University of Technology. Accessed August 03, 2020. http://resolver.tudelft.nl/uuid:9fd87374-ab6b-4574-a7f0-96dc27ff2eb0.

MLA Handbook (7th Edition):

Krishnamoorthi, Sathish (author). “Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression.” 2018. Web. 03 Aug 2020.

Vancouver:

Krishnamoorthi S(. Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Aug 03]. Available from: http://resolver.tudelft.nl/uuid:9fd87374-ab6b-4574-a7f0-96dc27ff2eb0.

Council of Science Editors:

Krishnamoorthi S(. Model-Based Compensation for Serial Manipulators through Semi-Parametric Gaussian Process Regression. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9fd87374-ab6b-4574-a7f0-96dc27ff2eb0


University of Manchester

3. Phillips, Nick. Modelling and analysis of oscillations in gene expression through neural development.

Degree: 2016, University of Manchester

The timing of differentiation underlies the development of any organ system. In neural development, the expression of the transcription factor Hes1 has been shown to… (more)

Subjects/Keywords: neural stem cells; stochasticity; gaussian process; differentiation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Phillips, N. (2016). Modelling and analysis of oscillations in gene expression through neural development. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629

Chicago Manual of Style (16th Edition):

Phillips, Nick. “Modelling and analysis of oscillations in gene expression through neural development.” 2016. Doctoral Dissertation, University of Manchester. Accessed August 03, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629.

MLA Handbook (7th Edition):

Phillips, Nick. “Modelling and analysis of oscillations in gene expression through neural development.” 2016. Web. 03 Aug 2020.

Vancouver:

Phillips N. Modelling and analysis of oscillations in gene expression through neural development. [Internet] [Doctoral dissertation]. University of Manchester; 2016. [cited 2020 Aug 03]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629.

Council of Science Editors:

Phillips N. Modelling and analysis of oscillations in gene expression through neural development. [Doctoral Dissertation]. University of Manchester; 2016. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629


University of Waikato

4. Ma, Jinjin. Parameter Tuning Using Gaussian Processes .

Degree: 2012, University of Waikato

 Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good… (more)

Subjects/Keywords: Parameter Tunning; Gaussian Process Optimization; Machine Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ma, J. (2012). Parameter Tuning Using Gaussian Processes . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/6497

Chicago Manual of Style (16th Edition):

Ma, Jinjin. “Parameter Tuning Using Gaussian Processes .” 2012. Masters Thesis, University of Waikato. Accessed August 03, 2020. http://hdl.handle.net/10289/6497.

MLA Handbook (7th Edition):

Ma, Jinjin. “Parameter Tuning Using Gaussian Processes .” 2012. Web. 03 Aug 2020.

Vancouver:

Ma J. Parameter Tuning Using Gaussian Processes . [Internet] [Masters thesis]. University of Waikato; 2012. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/10289/6497.

Council of Science Editors:

Ma J. Parameter Tuning Using Gaussian Processes . [Masters Thesis]. University of Waikato; 2012. Available from: http://hdl.handle.net/10289/6497


University of Sydney

5. Marchant Matus, Roman. Bayesian Optimisation for Planning in Dynamic Environments .

Degree: 2015, University of Sydney

 This thesis addresses the problem of trajectory planning for monitoring extreme values of an environmental phenomenon that changes in space and time. The most relevant… (more)

Subjects/Keywords: Bayesian; Optimisation; Planning; Robotics; POMDP; Gaussian-Process

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Marchant Matus, R. (2015). Bayesian Optimisation for Planning in Dynamic Environments . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/14497

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):

Marchant Matus, Roman. “Bayesian Optimisation for Planning in Dynamic Environments .” 2015. Thesis, University of Sydney. Accessed August 03, 2020. http://hdl.handle.net/2123/14497.

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

MLA Handbook (7th Edition):

Marchant Matus, Roman. “Bayesian Optimisation for Planning in Dynamic Environments .” 2015. Web. 03 Aug 2020.

Vancouver:

Marchant Matus R. Bayesian Optimisation for Planning in Dynamic Environments . [Internet] [Thesis]. University of Sydney; 2015. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/2123/14497.

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

Council of Science Editors:

Marchant Matus R. Bayesian Optimisation for Planning in Dynamic Environments . [Thesis]. University of Sydney; 2015. Available from: http://hdl.handle.net/2123/14497

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


University of Sydney

6. Wilson, Troy Daniel. Adaptive Sampling For Efficient Online Modelling .

Degree: 2017, University of Sydney

 This thesis examines methods enabling autonomous systems to make active sampling and planning decisions in real time. Gaussian Process (GP) regression is chosen as a… (more)

Subjects/Keywords: Planning; Entropy; Gaussian Process; Heteroscedastic; Autonomous

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wilson, T. D. (2017). Adaptive Sampling For Efficient Online Modelling . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17257

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):

Wilson, Troy Daniel. “Adaptive Sampling For Efficient Online Modelling .” 2017. Thesis, University of Sydney. Accessed August 03, 2020. http://hdl.handle.net/2123/17257.

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

MLA Handbook (7th Edition):

Wilson, Troy Daniel. “Adaptive Sampling For Efficient Online Modelling .” 2017. Web. 03 Aug 2020.

Vancouver:

Wilson TD. Adaptive Sampling For Efficient Online Modelling . [Internet] [Thesis]. University of Sydney; 2017. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/2123/17257.

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

Council of Science Editors:

Wilson TD. Adaptive Sampling For Efficient Online Modelling . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/17257

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


Delft University of Technology

7. Wesel, Frederiek (author). Sparse Gaussian Processes in the Longstaff-Schwartz algorithm.

Degree: 2019, Delft University of Technology

 In financial applications it is often necessary to determine conditional expectations in Monte Carlo type of simulations. The industry standard at the moment relies on… (more)

Subjects/Keywords: Gaussian process regression; Bermudan options; Longstaff-Schwartz

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wesel, F. (. (2019). Sparse Gaussian Processes in the Longstaff-Schwartz algorithm. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6cc65fdf-1268-49cd-b0ab-163088603ff7

Chicago Manual of Style (16th Edition):

Wesel, Frederiek (author). “Sparse Gaussian Processes in the Longstaff-Schwartz algorithm.” 2019. Masters Thesis, Delft University of Technology. Accessed August 03, 2020. http://resolver.tudelft.nl/uuid:6cc65fdf-1268-49cd-b0ab-163088603ff7.

MLA Handbook (7th Edition):

Wesel, Frederiek (author). “Sparse Gaussian Processes in the Longstaff-Schwartz algorithm.” 2019. Web. 03 Aug 2020.

Vancouver:

Wesel F(. Sparse Gaussian Processes in the Longstaff-Schwartz algorithm. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Aug 03]. Available from: http://resolver.tudelft.nl/uuid:6cc65fdf-1268-49cd-b0ab-163088603ff7.

Council of Science Editors:

Wesel F(. Sparse Gaussian Processes in the Longstaff-Schwartz algorithm. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:6cc65fdf-1268-49cd-b0ab-163088603ff7


Delft University of Technology

8. Turan, Taylan (author). Reduced Order Model Base Creation with Bayesian Optimization.

Degree: 2020, Delft University of Technology

 This research considers the offline training stage of the Reduced Order Models (ROM), that has been getting attention recently on the endeavor to come up… (more)

Subjects/Keywords: Bayesian Optimization; Reduced Order Model; Gaussian Process

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Turan, T. (. (2020). Reduced Order Model Base Creation with Bayesian Optimization. (Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:972caf56-3621-414b-a582-2561e2a5ff54

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):

Turan, Taylan (author). “Reduced Order Model Base Creation with Bayesian Optimization.” 2020. Thesis, Delft University of Technology. Accessed August 03, 2020. http://resolver.tudelft.nl/uuid:972caf56-3621-414b-a582-2561e2a5ff54.

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

MLA Handbook (7th Edition):

Turan, Taylan (author). “Reduced Order Model Base Creation with Bayesian Optimization.” 2020. Web. 03 Aug 2020.

Vancouver:

Turan T(. Reduced Order Model Base Creation with Bayesian Optimization. [Internet] [Thesis]. Delft University of Technology; 2020. [cited 2020 Aug 03]. Available from: http://resolver.tudelft.nl/uuid:972caf56-3621-414b-a582-2561e2a5ff54.

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

Council of Science Editors:

Turan T(. Reduced Order Model Base Creation with Bayesian Optimization. [Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:972caf56-3621-414b-a582-2561e2a5ff54

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


University of Arizona

9. Sharma, Yashika. Machine Learning and Additive Manufacturing Based Antenna Design Techniques .

Degree: 2020, University of Arizona

 This dissertation investigates the application of machine learning (ML) techniques to additive manufacturing (AM) technology with the ultimate goal of tackling the universal antenna design… (more)

Subjects/Keywords: Antenna; Gaussian Process; lasso; Machine Learning; Optimization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Sharma, Y. (2020). Machine Learning and Additive Manufacturing Based Antenna Design Techniques . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/637723

Chicago Manual of Style (16th Edition):

Sharma, Yashika. “Machine Learning and Additive Manufacturing Based Antenna Design Techniques .” 2020. Doctoral Dissertation, University of Arizona. Accessed August 03, 2020. http://hdl.handle.net/10150/637723.

MLA Handbook (7th Edition):

Sharma, Yashika. “Machine Learning and Additive Manufacturing Based Antenna Design Techniques .” 2020. Web. 03 Aug 2020.

Vancouver:

Sharma Y. Machine Learning and Additive Manufacturing Based Antenna Design Techniques . [Internet] [Doctoral dissertation]. University of Arizona; 2020. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/10150/637723.

Council of Science Editors:

Sharma Y. Machine Learning and Additive Manufacturing Based Antenna Design Techniques . [Doctoral Dissertation]. University of Arizona; 2020. Available from: http://hdl.handle.net/10150/637723


Duke University

10. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 August 03, 2020. 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. 03 Aug 2020.

Vancouver:

Wei H. Sensor Planning for Bayesian Nonparametric Target Modeling . [Internet] [Thesis]. Duke University; 2016. [cited 2020 Aug 03]. 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


Penn State University

11. Chang, Won. Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data.

Degree: PhD, Statistics, 2014, Penn State University

 This thesis focuses on statistical methods to calibrate complex computer models using high-dimensional spatial data sets. This work is motivated by important research problems in… (more)

Subjects/Keywords: Climate Model Calibration; Gaussian Process; High-dimensional Spatial Data; Non-Gaussian Spatial Data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chang, W. (2014). Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/22487

Chicago Manual of Style (16th Edition):

Chang, Won. “Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data.” 2014. Doctoral Dissertation, Penn State University. Accessed August 03, 2020. https://etda.libraries.psu.edu/catalog/22487.

MLA Handbook (7th Edition):

Chang, Won. “Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data.” 2014. Web. 03 Aug 2020.

Vancouver:

Chang W. Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data. [Internet] [Doctoral dissertation]. Penn State University; 2014. [cited 2020 Aug 03]. Available from: https://etda.libraries.psu.edu/catalog/22487.

Council of Science Editors:

Chang W. Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data. [Doctoral Dissertation]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/22487


Carnegie Mellon University

12. Castellanos, Lucia. Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms.

Degree: 2013, Carnegie Mellon University

 The primate hand, a biomechanical structure with over twenty kinematic degrees of freedom, has an elaborate anatomical architecture. Although the hand requires complex, coordinated neural… (more)

Subjects/Keywords: Variance Decomposition; Multivariate Gaussian Process Factor Analysis; Laplace Gaussian Filter; Functional Data Alignment; Encoding; Decoding

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Castellanos, L. (2013). Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/273

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):

Castellanos, Lucia. “Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms.” 2013. Thesis, Carnegie Mellon University. Accessed August 03, 2020. http://repository.cmu.edu/dissertations/273.

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

MLA Handbook (7th Edition):

Castellanos, Lucia. “Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms.” 2013. Web. 03 Aug 2020.

Vancouver:

Castellanos L. Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms. [Internet] [Thesis]. Carnegie Mellon University; 2013. [cited 2020 Aug 03]. Available from: http://repository.cmu.edu/dissertations/273.

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

Council of Science Editors:

Castellanos L. Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms. [Thesis]. Carnegie Mellon University; 2013. Available from: http://repository.cmu.edu/dissertations/273

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


University of Alberta

13. Ranjan, Rishik. Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization.

Degree: MS, Department of Chemical and Materials Engineering, 2015, University of Alberta

 Availability of large amounts of industrial process data is allowing researchers to explore new data-based modelling methods. In this thesis, Gaussian process (GP) regression, a… (more)

Subjects/Keywords: SAGD; Optimization; Outliers; Robust identification; Gaussian process regression; EM algorithm

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ranjan, R. (2015). Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/b2773z58w

Chicago Manual of Style (16th Edition):

Ranjan, Rishik. “Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization.” 2015. Masters Thesis, University of Alberta. Accessed August 03, 2020. https://era.library.ualberta.ca/files/b2773z58w.

MLA Handbook (7th Edition):

Ranjan, Rishik. “Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization.” 2015. Web. 03 Aug 2020.

Vancouver:

Ranjan R. Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2020 Aug 03]. Available from: https://era.library.ualberta.ca/files/b2773z58w.

Council of Science Editors:

Ranjan R. Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/b2773z58w


Texas A&M University

14. Kumari, Deepika. A Data-driven Approach to Power System Dynamic State Estimation.

Degree: MS, Electrical Engineering, 2017, Texas A&M University

 State estimation is a key function in the supervisory control and planning of an electric power grid. Typically, the independent system operator (ISO) runs least-squares… (more)

Subjects/Keywords: Dynamic state estimation; Power systems; Unscented Kalman filter; Gaussian process

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kumari, D. (2017). A Data-driven Approach to Power System Dynamic State Estimation. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/166100

Chicago Manual of Style (16th Edition):

Kumari, Deepika. “A Data-driven Approach to Power System Dynamic State Estimation.” 2017. Masters Thesis, Texas A&M University. Accessed August 03, 2020. http://hdl.handle.net/1969.1/166100.

MLA Handbook (7th Edition):

Kumari, Deepika. “A Data-driven Approach to Power System Dynamic State Estimation.” 2017. Web. 03 Aug 2020.

Vancouver:

Kumari D. A Data-driven Approach to Power System Dynamic State Estimation. [Internet] [Masters thesis]. Texas A&M University; 2017. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/1969.1/166100.

Council of Science Editors:

Kumari D. A Data-driven Approach to Power System Dynamic State Estimation. [Masters Thesis]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/166100


Brigham Young University

15. Ferguson, Bradley Thomas. Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models.

Degree: MS, 2011, Brigham Young University

 Detection of biological and chemical threats is an important consideration in the modern national defense policy. Much of the testing and evaluation of threat detection… (more)

Subjects/Keywords: Gaussian process; bayesian; adaptive design; Statistics and Probability

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ferguson, B. T. (2011). Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3727&context=etd

Chicago Manual of Style (16th Edition):

Ferguson, Bradley Thomas. “Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models.” 2011. Masters Thesis, Brigham Young University. Accessed August 03, 2020. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3727&context=etd.

MLA Handbook (7th Edition):

Ferguson, Bradley Thomas. “Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models.” 2011. Web. 03 Aug 2020.

Vancouver:

Ferguson BT. Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models. [Internet] [Masters thesis]. Brigham Young University; 2011. [cited 2020 Aug 03]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3727&context=etd.

Council of Science Editors:

Ferguson BT. Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models. [Masters Thesis]. Brigham Young University; 2011. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3727&context=etd


The Ohio State University

16. Kumar, Arun. Sequential Calibration Of Computer Models.

Degree: PhD, Statistics, 2008, The Ohio State University

  Computer experiments are becoming popular for their ability to simulate complex physical systems at relatively cheap cost. Since these computer experiments may run very… (more)

Subjects/Keywords: Statistics; Computer Experiments; Calibration; Space filling designs; Gaussian Process Models

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kumar, A. (2008). Sequential Calibration Of Computer Models. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1218568898

Chicago Manual of Style (16th Edition):

Kumar, Arun. “Sequential Calibration Of Computer Models.” 2008. Doctoral Dissertation, The Ohio State University. Accessed August 03, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218568898.

MLA Handbook (7th Edition):

Kumar, Arun. “Sequential Calibration Of Computer Models.” 2008. Web. 03 Aug 2020.

Vancouver:

Kumar A. Sequential Calibration Of Computer Models. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2020 Aug 03]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1218568898.

Council of Science Editors:

Kumar A. Sequential Calibration Of Computer Models. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1218568898


The Ohio State University

17. Shah, Siddharth S. Robust Heart Rate Variability Analysis using Gaussian Process Regression.

Degree: MS, Electrical and Computer Engineering, 2011, The Ohio State University

 Heart rate variability (HRV) is a non-invasive way of measuring autonomic nervoussystem dynamics as influenced by one's emotional state. By studying beat to beat variations,… (more)

Subjects/Keywords: Electrical Engineering; Heart Rate Variability; Gaussian Process Regression

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shah, S. S. (2011). Robust Heart Rate Variability Analysis using Gaussian Process Regression. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1293737259

Chicago Manual of Style (16th Edition):

Shah, Siddharth S. “Robust Heart Rate Variability Analysis using Gaussian Process Regression.” 2011. Masters Thesis, The Ohio State University. Accessed August 03, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1293737259.

MLA Handbook (7th Edition):

Shah, Siddharth S. “Robust Heart Rate Variability Analysis using Gaussian Process Regression.” 2011. Web. 03 Aug 2020.

Vancouver:

Shah SS. Robust Heart Rate Variability Analysis using Gaussian Process Regression. [Internet] [Masters thesis]. The Ohio State University; 2011. [cited 2020 Aug 03]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1293737259.

Council of Science Editors:

Shah SS. Robust Heart Rate Variability Analysis using Gaussian Process Regression. [Masters Thesis]. The Ohio State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1293737259


Virginia Tech

18. Xie, Guangrui. Robust and Data-Efficient Metamodel-Based Approaches for Online Analysis of Time-Dependent Systems.

Degree: PhD, Industrial and Systems Engineering, 2020, Virginia Tech

 Metamodeling has been regarded as a powerful analysis tool to learn the input-output relationship of an engineering system with a limited amount of experimental data… (more)

Subjects/Keywords: Metamodeling; Gaussian process regression; load forecasting; trajectory prediction; uniform error bounds

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Xie, G. (2020). Robust and Data-Efficient Metamodel-Based Approaches for Online Analysis of Time-Dependent Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/98806

Chicago Manual of Style (16th Edition):

Xie, Guangrui. “Robust and Data-Efficient Metamodel-Based Approaches for Online Analysis of Time-Dependent Systems.” 2020. Doctoral Dissertation, Virginia Tech. Accessed August 03, 2020. http://hdl.handle.net/10919/98806.

MLA Handbook (7th Edition):

Xie, Guangrui. “Robust and Data-Efficient Metamodel-Based Approaches for Online Analysis of Time-Dependent Systems.” 2020. Web. 03 Aug 2020.

Vancouver:

Xie G. Robust and Data-Efficient Metamodel-Based Approaches for Online Analysis of Time-Dependent Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/10919/98806.

Council of Science Editors:

Xie G. Robust and Data-Efficient Metamodel-Based Approaches for Online Analysis of Time-Dependent Systems. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/98806

19. Contal, Emile. Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization.

Degree: Docteur es, Mathématiques appliquées, 2016, Université Paris-Saclay (ComUE)

Cette thèse se consacre à une analyse rigoureuse des algorithmes d'optimisation globale équentielle. On se place dans un modèle de bandits stochastiques où un agent… (more)

Subjects/Keywords: Apprentissage statistique; Optimisation; Processus gaussien; Statistical learning; Optimization; Gaussian process

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Contal, E. (2016). Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2016SACLN038

Chicago Manual of Style (16th Edition):

Contal, Emile. “Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization.” 2016. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed August 03, 2020. http://www.theses.fr/2016SACLN038.

MLA Handbook (7th Edition):

Contal, Emile. “Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization.” 2016. Web. 03 Aug 2020.

Vancouver:

Contal E. Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2016. [cited 2020 Aug 03]. Available from: http://www.theses.fr/2016SACLN038.

Council of Science Editors:

Contal E. Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2016. Available from: http://www.theses.fr/2016SACLN038


Virginia Tech

20. Zielinski, Jacob Jonathan. Adapting Response Surface Methods for the Optimization of Black-Box Systems.

Degree: PhD, Statistics, 2010, Virginia Tech

 Complex mathematical models are often built to describe a physical process that would otherwise be extremely difficult, too costly or sometimes impossible to analyze. Generally,… (more)

Subjects/Keywords: Optimization; Gaussian Stochastic Process; Computer Experiments; Bayesian; Response Surface; DACE; Kriging

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Zielinski, J. J. (2010). Adapting Response Surface Methods for the Optimization of Black-Box Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/39295

Chicago Manual of Style (16th Edition):

Zielinski, Jacob Jonathan. “Adapting Response Surface Methods for the Optimization of Black-Box Systems.” 2010. Doctoral Dissertation, Virginia Tech. Accessed August 03, 2020. http://hdl.handle.net/10919/39295.

MLA Handbook (7th Edition):

Zielinski, Jacob Jonathan. “Adapting Response Surface Methods for the Optimization of Black-Box Systems.” 2010. Web. 03 Aug 2020.

Vancouver:

Zielinski JJ. Adapting Response Surface Methods for the Optimization of Black-Box Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2010. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/10919/39295.

Council of Science Editors:

Zielinski JJ. Adapting Response Surface Methods for the Optimization of Black-Box Systems. [Doctoral Dissertation]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/39295


University of Cambridge

21. Svensson, Valentine. Probabilistic modelling of cellular development from single-cell gene expression.

Degree: PhD, 2017, University of Cambridge

 The recent technology of single-cell RNA sequencing can be used to investigate molecular, transcriptional, changes in cells as they develop. I reviewed the literature on… (more)

Subjects/Keywords: cellular differentiation; gaussian process; cellular development; single cell; rna-sequencing

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Svensson, V. (2017). Probabilistic modelling of cellular development from single-cell gene expression. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/267937

Chicago Manual of Style (16th Edition):

Svensson, Valentine. “Probabilistic modelling of cellular development from single-cell gene expression.” 2017. Doctoral Dissertation, University of Cambridge. Accessed August 03, 2020. https://www.repository.cam.ac.uk/handle/1810/267937.

MLA Handbook (7th Edition):

Svensson, Valentine. “Probabilistic modelling of cellular development from single-cell gene expression.” 2017. Web. 03 Aug 2020.

Vancouver:

Svensson V. Probabilistic modelling of cellular development from single-cell gene expression. [Internet] [Doctoral dissertation]. University of Cambridge; 2017. [cited 2020 Aug 03]. Available from: https://www.repository.cam.ac.uk/handle/1810/267937.

Council of Science Editors:

Svensson V. Probabilistic modelling of cellular development from single-cell gene expression. [Doctoral Dissertation]. University of Cambridge; 2017. Available from: https://www.repository.cam.ac.uk/handle/1810/267937


University of Cambridge

22. Bruinsma, Wessel. The Generalised Gaussian Process Convolution Model.

Degree: MPhil, 2016, University of Cambridge

 This thesis formulates the Generalised Gaussian Process Convolution Model (GGPCM), which is a generalisation of the Gaussian Process Convolution Model presented by Tobar et al.… (more)

Subjects/Keywords: machine learning; Gaussian process; kernel; nonparametric kernel; multi-task learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Bruinsma, W. (2016). The Generalised Gaussian Process Convolution Model. (Masters Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273357

Chicago Manual of Style (16th Edition):

Bruinsma, Wessel. “The Generalised Gaussian Process Convolution Model.” 2016. Masters Thesis, University of Cambridge. Accessed August 03, 2020. https://www.repository.cam.ac.uk/handle/1810/273357.

MLA Handbook (7th Edition):

Bruinsma, Wessel. “The Generalised Gaussian Process Convolution Model.” 2016. Web. 03 Aug 2020.

Vancouver:

Bruinsma W. The Generalised Gaussian Process Convolution Model. [Internet] [Masters thesis]. University of Cambridge; 2016. [cited 2020 Aug 03]. Available from: https://www.repository.cam.ac.uk/handle/1810/273357.

Council of Science Editors:

Bruinsma W. The Generalised Gaussian Process Convolution Model. [Masters Thesis]. University of Cambridge; 2016. Available from: https://www.repository.cam.ac.uk/handle/1810/273357


University of Colorado

23. Kang, Yoonsuk. Bayesian Based Parameter Identification for Building Energy Models.

Degree: PhD, 2014, University of Colorado

  In this research work, a series of sensitivity analyses were performed to validate the proposed Bayesian approach to identify unknown parameters in building energy… (more)

Subjects/Keywords: Bayesian; existing building; Gaussian process; parameter identification; prior belief; Architectural Engineering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kang, Y. (2014). Bayesian Based Parameter Identification for Building Energy Models. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/cven_gradetds/115

Chicago Manual of Style (16th Edition):

Kang, Yoonsuk. “Bayesian Based Parameter Identification for Building Energy Models.” 2014. Doctoral Dissertation, University of Colorado. Accessed August 03, 2020. https://scholar.colorado.edu/cven_gradetds/115.

MLA Handbook (7th Edition):

Kang, Yoonsuk. “Bayesian Based Parameter Identification for Building Energy Models.” 2014. Web. 03 Aug 2020.

Vancouver:

Kang Y. Bayesian Based Parameter Identification for Building Energy Models. [Internet] [Doctoral dissertation]. University of Colorado; 2014. [cited 2020 Aug 03]. Available from: https://scholar.colorado.edu/cven_gradetds/115.

Council of Science Editors:

Kang Y. Bayesian Based Parameter Identification for Building Energy Models. [Doctoral Dissertation]. University of Colorado; 2014. Available from: https://scholar.colorado.edu/cven_gradetds/115


University of Colorado

24. Wagle, Neeti. Transfer Learning for Characterization of Small Unmanned Aircraft Communication.

Degree: PhD, Computer Science, 2015, University of Colorado

  This dissertation develops a nonparametric, computationally efficient method for modeling the airborne communication environment for small unmanned aircraft systems (sUAS). Transfer learning for Gaussian(more)

Subjects/Keywords: Gaussian Process; Machine Learning; Modeling; Artificial Intelligence and Robotics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wagle, N. (2015). Transfer Learning for Characterization of Small Unmanned Aircraft Communication. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/100

Chicago Manual of Style (16th Edition):

Wagle, Neeti. “Transfer Learning for Characterization of Small Unmanned Aircraft Communication.” 2015. Doctoral Dissertation, University of Colorado. Accessed August 03, 2020. https://scholar.colorado.edu/csci_gradetds/100.

MLA Handbook (7th Edition):

Wagle, Neeti. “Transfer Learning for Characterization of Small Unmanned Aircraft Communication.” 2015. Web. 03 Aug 2020.

Vancouver:

Wagle N. Transfer Learning for Characterization of Small Unmanned Aircraft Communication. [Internet] [Doctoral dissertation]. University of Colorado; 2015. [cited 2020 Aug 03]. Available from: https://scholar.colorado.edu/csci_gradetds/100.

Council of Science Editors:

Wagle N. Transfer Learning for Characterization of Small Unmanned Aircraft Communication. [Doctoral Dissertation]. University of Colorado; 2015. Available from: https://scholar.colorado.edu/csci_gradetds/100

25. XU NUO. ONLINE GAUSSIAN PROCESS FILTERING FOR PERSISTENT ROBOT LOCALIZATION WITH ARBITRARY SENSOR MODALITIES.

Degree: 2016, National University of Singapore

Subjects/Keywords: Localization; Gaussian Process; Robot; Filtering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

NUO, X. (2016). ONLINE GAUSSIAN PROCESS FILTERING FOR PERSISTENT ROBOT LOCALIZATION WITH ARBITRARY SENSOR MODALITIES. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/135600

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):

NUO, XU. “ONLINE GAUSSIAN PROCESS FILTERING FOR PERSISTENT ROBOT LOCALIZATION WITH ARBITRARY SENSOR MODALITIES.” 2016. Thesis, National University of Singapore. Accessed August 03, 2020. http://scholarbank.nus.edu.sg/handle/10635/135600.

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

MLA Handbook (7th Edition):

NUO, XU. “ONLINE GAUSSIAN PROCESS FILTERING FOR PERSISTENT ROBOT LOCALIZATION WITH ARBITRARY SENSOR MODALITIES.” 2016. Web. 03 Aug 2020.

Vancouver:

NUO X. ONLINE GAUSSIAN PROCESS FILTERING FOR PERSISTENT ROBOT LOCALIZATION WITH ARBITRARY SENSOR MODALITIES. [Internet] [Thesis]. National University of Singapore; 2016. [cited 2020 Aug 03]. Available from: http://scholarbank.nus.edu.sg/handle/10635/135600.

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

Council of Science Editors:

NUO X. ONLINE GAUSSIAN PROCESS FILTERING FOR PERSISTENT ROBOT LOCALIZATION WITH ARBITRARY SENSOR MODALITIES. [Thesis]. National University of Singapore; 2016. Available from: http://scholarbank.nus.edu.sg/handle/10635/135600

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


University of Exeter

26. Wood, Michael James. An exploration of building design and optimisation methods using Kriging meta-modelling.

Degree: PhD, 2016, University of Exeter

 This thesis investigates the application of Kriging meta-modelling techniques in the field of building design and optimisation. In conducting this research, there were two key… (more)

Subjects/Keywords: 690; Building optimisation; uncertainty analysis; kriging; gaussian process

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wood, M. J. (2016). An exploration of building design and optimisation methods using Kriging meta-modelling. (Doctoral Dissertation). University of Exeter. Retrieved from http://hdl.handle.net/10871/24974

Chicago Manual of Style (16th Edition):

Wood, Michael James. “An exploration of building design and optimisation methods using Kriging meta-modelling.” 2016. Doctoral Dissertation, University of Exeter. Accessed August 03, 2020. http://hdl.handle.net/10871/24974.

MLA Handbook (7th Edition):

Wood, Michael James. “An exploration of building design and optimisation methods using Kriging meta-modelling.” 2016. Web. 03 Aug 2020.

Vancouver:

Wood MJ. An exploration of building design and optimisation methods using Kriging meta-modelling. [Internet] [Doctoral dissertation]. University of Exeter; 2016. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/10871/24974.

Council of Science Editors:

Wood MJ. An exploration of building design and optimisation methods using Kriging meta-modelling. [Doctoral Dissertation]. University of Exeter; 2016. Available from: http://hdl.handle.net/10871/24974


University of Manchester

27. Ahmed, Sumon. Scalable Gaussian process methods for single-cell data.

Degree: PhD, 2020, University of Manchester

 The analysis of single-cell data creates the opportunity to examine the temporal dynamics of complex biological processes where the generation of time course experiments is… (more)

Subjects/Keywords: gene expression; branching; differential expression; single-cell; Gaussian process; pseudotime

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ahmed, S. (2020). Scalable Gaussian process methods for single-cell data. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/scalable-gaussian-process-methods-for-singlecell-data(2ae3ceac-66dd-4c11-b5fc-250a4aed2239).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799481

Chicago Manual of Style (16th Edition):

Ahmed, Sumon. “Scalable Gaussian process methods for single-cell data.” 2020. Doctoral Dissertation, University of Manchester. Accessed August 03, 2020. https://www.research.manchester.ac.uk/portal/en/theses/scalable-gaussian-process-methods-for-singlecell-data(2ae3ceac-66dd-4c11-b5fc-250a4aed2239).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799481.

MLA Handbook (7th Edition):

Ahmed, Sumon. “Scalable Gaussian process methods for single-cell data.” 2020. Web. 03 Aug 2020.

Vancouver:

Ahmed S. Scalable Gaussian process methods for single-cell data. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2020 Aug 03]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/scalable-gaussian-process-methods-for-singlecell-data(2ae3ceac-66dd-4c11-b5fc-250a4aed2239).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799481.

Council of Science Editors:

Ahmed S. Scalable Gaussian process methods for single-cell data. [Doctoral Dissertation]. University of Manchester; 2020. Available from: https://www.research.manchester.ac.uk/portal/en/theses/scalable-gaussian-process-methods-for-singlecell-data(2ae3ceac-66dd-4c11-b5fc-250a4aed2239).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799481


Virginia Tech

28. Fadikar, Arindam. Stochastic Computer Model Calibration and Uncertainty Quantification.

Degree: PhD, Statistics, 2019, Virginia Tech

 Mathematical models are versatile and often provide accurate description of physical events. Scientific models are used to study such events in order to gain understanding… (more)

Subjects/Keywords: Computer model; gaussian process; sensitivity analysis; epidemiology; bayesian estimation; mcmc

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Fadikar, A. (2019). Stochastic Computer Model Calibration and Uncertainty Quantification. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/91985

Chicago Manual of Style (16th Edition):

Fadikar, Arindam. “Stochastic Computer Model Calibration and Uncertainty Quantification.” 2019. Doctoral Dissertation, Virginia Tech. Accessed August 03, 2020. http://hdl.handle.net/10919/91985.

MLA Handbook (7th Edition):

Fadikar, Arindam. “Stochastic Computer Model Calibration and Uncertainty Quantification.” 2019. Web. 03 Aug 2020.

Vancouver:

Fadikar A. Stochastic Computer Model Calibration and Uncertainty Quantification. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/10919/91985.

Council of Science Editors:

Fadikar A. Stochastic Computer Model Calibration and Uncertainty Quantification. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/91985


University of Manchester

29. Ahmed, Sumon. SCALABLE GAUSSIAN PROCESS METHODS FOR SINGLE-CELL DATA.

Degree: 2020, University of Manchester

 The analysis of single-cell data creates the opportunity to examine the temporal dynamics of complex biological processes where the generation of time course experiments is… (more)

Subjects/Keywords: Gaussian process; single-cell; pseudotime; branching; gene expression; differential expression

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ahmed, S. (2020). SCALABLE GAUSSIAN PROCESS METHODS FOR SINGLE-CELL DATA. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323186

Chicago Manual of Style (16th Edition):

Ahmed, Sumon. “SCALABLE GAUSSIAN PROCESS METHODS FOR SINGLE-CELL DATA.” 2020. Doctoral Dissertation, University of Manchester. Accessed August 03, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323186.

MLA Handbook (7th Edition):

Ahmed, Sumon. “SCALABLE GAUSSIAN PROCESS METHODS FOR SINGLE-CELL DATA.” 2020. Web. 03 Aug 2020.

Vancouver:

Ahmed S. SCALABLE GAUSSIAN PROCESS METHODS FOR SINGLE-CELL DATA. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2020 Aug 03]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323186.

Council of Science Editors:

Ahmed S. SCALABLE GAUSSIAN PROCESS METHODS FOR SINGLE-CELL DATA. [Doctoral Dissertation]. University of Manchester; 2020. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323186


University of Michigan

30. Quann, Michael. Ground Robot Energy Prediction and Reachability in Off-Road Environments Through Spatial Terrain Mapping.

Degree: PhD, Mechanical Engineering, 2019, University of Michigan

 For robotic applications, energy is a key resource that can both enable and limit the tasks that a robot can perform in an environment. In… (more)

Subjects/Keywords: Robotic energy prediction; Gaussian process regression; Mechanical Engineering; Engineering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Quann, M. (2019). Ground Robot Energy Prediction and Reachability in Off-Road Environments Through Spatial Terrain Mapping. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/153361

Chicago Manual of Style (16th Edition):

Quann, Michael. “Ground Robot Energy Prediction and Reachability in Off-Road Environments Through Spatial Terrain Mapping.” 2019. Doctoral Dissertation, University of Michigan. Accessed August 03, 2020. http://hdl.handle.net/2027.42/153361.

MLA Handbook (7th Edition):

Quann, Michael. “Ground Robot Energy Prediction and Reachability in Off-Road Environments Through Spatial Terrain Mapping.” 2019. Web. 03 Aug 2020.

Vancouver:

Quann M. Ground Robot Energy Prediction and Reachability in Off-Road Environments Through Spatial Terrain Mapping. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Aug 03]. Available from: http://hdl.handle.net/2027.42/153361.

Council of Science Editors:

Quann M. Ground Robot Energy Prediction and Reachability in Off-Road Environments Through Spatial Terrain Mapping. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/153361

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

.