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You searched for subject:(Bayesian models). Showing records 1 – 30 of 777 total matches.

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1. Pagani Zanini, Carlos Tadeu. Dependent mixtures and random partitions.

Degree: PhD, Statistics, 2019, University of Texas – Austin

 This work develops new methodology for Bayesian dependent mixture models and dependent random partitions with applications to biomedical data. A mixture model implies a random… (more)

Subjects/Keywords: Dependent mixture models; Bayesian models

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

APA (6th Edition):

Pagani Zanini, C. T. (2019). Dependent mixtures and random partitions. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/3274

Chicago Manual of Style (16th Edition):

Pagani Zanini, Carlos Tadeu. “Dependent mixtures and random partitions.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed November 28, 2020. http://dx.doi.org/10.26153/tsw/3274.

MLA Handbook (7th Edition):

Pagani Zanini, Carlos Tadeu. “Dependent mixtures and random partitions.” 2019. Web. 28 Nov 2020.

Vancouver:

Pagani Zanini CT. Dependent mixtures and random partitions. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Nov 28]. Available from: http://dx.doi.org/10.26153/tsw/3274.

Council of Science Editors:

Pagani Zanini CT. Dependent mixtures and random partitions. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/3274


North Carolina State University

2. Krachey, Matthew James. Hierarchical Bayesian application to instantaneous rates tag-return models.

Degree: PhD, Zoology, 2009, North Carolina State University

 Natural mortality has always been a challenging quantity to estimate in harvested populations. The most common approaches to estimation include a regression model based on… (more)

Subjects/Keywords: Bayesian; Tag-return models

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APA (6th Edition):

Krachey, M. J. (2009). Hierarchical Bayesian application to instantaneous rates tag-return models. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/4299

Chicago Manual of Style (16th Edition):

Krachey, Matthew James. “Hierarchical Bayesian application to instantaneous rates tag-return models.” 2009. Doctoral Dissertation, North Carolina State University. Accessed November 28, 2020. http://www.lib.ncsu.edu/resolver/1840.16/4299.

MLA Handbook (7th Edition):

Krachey, Matthew James. “Hierarchical Bayesian application to instantaneous rates tag-return models.” 2009. Web. 28 Nov 2020.

Vancouver:

Krachey MJ. Hierarchical Bayesian application to instantaneous rates tag-return models. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2020 Nov 28]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4299.

Council of Science Editors:

Krachey MJ. Hierarchical Bayesian application to instantaneous rates tag-return models. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4299


Queensland University of Technology

3. Rolfe, Margaret Irene. Bayesian models for longitudinal data.

Degree: 2010, Queensland University of Technology

 Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes… (more)

Subjects/Keywords: Bayesian models; longitudinal data

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APA (6th Edition):

Rolfe, M. I. (2010). Bayesian models for longitudinal data. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/34435/

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):

Rolfe, Margaret Irene. “Bayesian models for longitudinal data.” 2010. Thesis, Queensland University of Technology. Accessed November 28, 2020. https://eprints.qut.edu.au/34435/.

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

MLA Handbook (7th Edition):

Rolfe, Margaret Irene. “Bayesian models for longitudinal data.” 2010. Web. 28 Nov 2020.

Vancouver:

Rolfe MI. Bayesian models for longitudinal data. [Internet] [Thesis]. Queensland University of Technology; 2010. [cited 2020 Nov 28]. Available from: https://eprints.qut.edu.au/34435/.

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

Council of Science Editors:

Rolfe MI. Bayesian models for longitudinal data. [Thesis]. Queensland University of Technology; 2010. Available from: https://eprints.qut.edu.au/34435/

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


Queensland University of Technology

4. Donald, Margaret. Using Bayesian methods for the estimation of uncertainty in complex statistical models.

Degree: 2011, Queensland University of Technology

 The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal… (more)

Subjects/Keywords: Bayesian methods; complex statistical models

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APA (6th Edition):

Donald, M. (2011). Using Bayesian methods for the estimation of uncertainty in complex statistical models. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/47132/

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):

Donald, Margaret. “Using Bayesian methods for the estimation of uncertainty in complex statistical models.” 2011. Thesis, Queensland University of Technology. Accessed November 28, 2020. https://eprints.qut.edu.au/47132/.

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

MLA Handbook (7th Edition):

Donald, Margaret. “Using Bayesian methods for the estimation of uncertainty in complex statistical models.” 2011. Web. 28 Nov 2020.

Vancouver:

Donald M. Using Bayesian methods for the estimation of uncertainty in complex statistical models. [Internet] [Thesis]. Queensland University of Technology; 2011. [cited 2020 Nov 28]. Available from: https://eprints.qut.edu.au/47132/.

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

Council of Science Editors:

Donald M. Using Bayesian methods for the estimation of uncertainty in complex statistical models. [Thesis]. Queensland University of Technology; 2011. Available from: https://eprints.qut.edu.au/47132/

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

5. Bratières, Sébastien. Non-parametric Bayesian models for structured output prediction.

Degree: PhD, 2018, University of Cambridge

 Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple,… (more)

Subjects/Keywords: machine learning; Bayesian models; probability

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

APA (6th Edition):

Bratières, S. (2018). Non-parametric Bayesian models for structured output prediction. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/274973

Chicago Manual of Style (16th Edition):

Bratières, Sébastien. “Non-parametric Bayesian models for structured output prediction.” 2018. Doctoral Dissertation, University of Cambridge. Accessed November 28, 2020. https://www.repository.cam.ac.uk/handle/1810/274973.

MLA Handbook (7th Edition):

Bratières, Sébastien. “Non-parametric Bayesian models for structured output prediction.” 2018. Web. 28 Nov 2020.

Vancouver:

Bratières S. Non-parametric Bayesian models for structured output prediction. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2020 Nov 28]. Available from: https://www.repository.cam.ac.uk/handle/1810/274973.

Council of Science Editors:

Bratières S. Non-parametric Bayesian models for structured output prediction. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/274973


University of Melbourne

6. Li, Yuan. Probabilistic models for aggregating crowdsourced annotations.

Degree: 2019, University of Melbourne

 This thesis explores aggregation methods for crowdsourced annotations. Crowdsourcing is a popular means of creating training and evaluation datasets for machine learning, e.g. used for… (more)

Subjects/Keywords: crowdsourcing; probabilistic models; Bayesian inference

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APA (6th Edition):

Li, Y. (2019). Probabilistic models for aggregating crowdsourced annotations. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/227106

Chicago Manual of Style (16th Edition):

Li, Yuan. “Probabilistic models for aggregating crowdsourced annotations.” 2019. Doctoral Dissertation, University of Melbourne. Accessed November 28, 2020. http://hdl.handle.net/11343/227106.

MLA Handbook (7th Edition):

Li, Yuan. “Probabilistic models for aggregating crowdsourced annotations.” 2019. Web. 28 Nov 2020.

Vancouver:

Li Y. Probabilistic models for aggregating crowdsourced annotations. [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/11343/227106.

Council of Science Editors:

Li Y. Probabilistic models for aggregating crowdsourced annotations. [Doctoral Dissertation]. University of Melbourne; 2019. Available from: http://hdl.handle.net/11343/227106


University of New South Wales

7. Pullen, James. A Bayesian approach to mixture models and transdimensional Markov chains.

Degree: Economics, 2011, University of New South Wales

 A general Bayesian sampling method is developed that uses parallel chains to select betweenmodels and to average the predictive density over such models. The method… (more)

Subjects/Keywords: MCMC; Bayesian; Mixture models

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

APA (6th Edition):

Pullen, J. (2011). A Bayesian approach to mixture models and transdimensional Markov chains. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/51520 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10207/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Pullen, James. “A Bayesian approach to mixture models and transdimensional Markov chains.” 2011. Doctoral Dissertation, University of New South Wales. Accessed November 28, 2020. http://handle.unsw.edu.au/1959.4/51520 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10207/SOURCE02?view=true.

MLA Handbook (7th Edition):

Pullen, James. “A Bayesian approach to mixture models and transdimensional Markov chains.” 2011. Web. 28 Nov 2020.

Vancouver:

Pullen J. A Bayesian approach to mixture models and transdimensional Markov chains. [Internet] [Doctoral dissertation]. University of New South Wales; 2011. [cited 2020 Nov 28]. Available from: http://handle.unsw.edu.au/1959.4/51520 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10207/SOURCE02?view=true.

Council of Science Editors:

Pullen J. A Bayesian approach to mixture models and transdimensional Markov chains. [Doctoral Dissertation]. University of New South Wales; 2011. Available from: http://handle.unsw.edu.au/1959.4/51520 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10207/SOURCE02?view=true


Duke University

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

Degree: 2013, Duke University

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

PETRALIA, FRANCESCA. “Structured Bayesian learning through mixture models .” 2013. Thesis, Duke University. Accessed November 28, 2020. http://hdl.handle.net/10161/8065.

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

MLA Handbook (7th Edition):

PETRALIA, FRANCESCA. “Structured Bayesian learning through mixture models .” 2013. Web. 28 Nov 2020.

Vancouver:

PETRALIA F. Structured Bayesian learning through mixture models . [Internet] [Thesis]. Duke University; 2013. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10161/8065.

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

Council of Science Editors:

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

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


University of Alberta

9. Khatibisepehr, Shima. Bayesian Solutions to Multi-model Inferential Sensing Problems.

Degree: PhD, Department of Chemical and Materials Engineering, 2013, University of Alberta

 In many industrial plants, development and implementation of advanced monitoring and control techniques require real-time measurement of process quality variables. However, on-line acquisition of such… (more)

Subjects/Keywords: Bayesian Soft Sensor; Multiple Models; Inferential Sensor

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

APA (6th Edition):

Khatibisepehr, S. (2013). Bayesian Solutions to Multi-model Inferential Sensing Problems. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/j6731485d

Chicago Manual of Style (16th Edition):

Khatibisepehr, Shima. “Bayesian Solutions to Multi-model Inferential Sensing Problems.” 2013. Doctoral Dissertation, University of Alberta. Accessed November 28, 2020. https://era.library.ualberta.ca/files/j6731485d.

MLA Handbook (7th Edition):

Khatibisepehr, Shima. “Bayesian Solutions to Multi-model Inferential Sensing Problems.” 2013. Web. 28 Nov 2020.

Vancouver:

Khatibisepehr S. Bayesian Solutions to Multi-model Inferential Sensing Problems. [Internet] [Doctoral dissertation]. University of Alberta; 2013. [cited 2020 Nov 28]. Available from: https://era.library.ualberta.ca/files/j6731485d.

Council of Science Editors:

Khatibisepehr S. Bayesian Solutions to Multi-model Inferential Sensing Problems. [Doctoral Dissertation]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/j6731485d


Nelson Mandela Metropolitan University

10. Sarpong, Abeam Danso. Tolerance intervals for variance component models using a Bayesian simulation procedure.

Degree: Faculty of Science, 2013, Nelson Mandela Metropolitan University

 The estimation of variance components serves as an integral part of the evaluation of variation, and is of interest and required in a variety of… (more)

Subjects/Keywords: Bayesian statistical decision theory; Multilevel models (Statistics)

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APA (6th Edition):

Sarpong, A. D. (2013). Tolerance intervals for variance component models using a Bayesian simulation procedure. (Thesis). Nelson Mandela Metropolitan University. Retrieved from http://hdl.handle.net/10948/d1021025

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):

Sarpong, Abeam Danso. “Tolerance intervals for variance component models using a Bayesian simulation procedure.” 2013. Thesis, Nelson Mandela Metropolitan University. Accessed November 28, 2020. http://hdl.handle.net/10948/d1021025.

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

MLA Handbook (7th Edition):

Sarpong, Abeam Danso. “Tolerance intervals for variance component models using a Bayesian simulation procedure.” 2013. Web. 28 Nov 2020.

Vancouver:

Sarpong AD. Tolerance intervals for variance component models using a Bayesian simulation procedure. [Internet] [Thesis]. Nelson Mandela Metropolitan University; 2013. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10948/d1021025.

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

Council of Science Editors:

Sarpong AD. Tolerance intervals for variance component models using a Bayesian simulation procedure. [Thesis]. Nelson Mandela Metropolitan University; 2013. Available from: http://hdl.handle.net/10948/d1021025

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


Cornell University

11. Wan, Muting. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics.

Degree: PhD, Statistics, 2015, Cornell University

 In recent years, sparse classification problems have emerged in many fields of study. Finite mixture models have been developed to facilitate Bayesian inference where parameter… (more)

Subjects/Keywords: Bayesian inference; Linear mixed models; Bioinformatics

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

APA (6th Edition):

Wan, M. (2015). Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/39389

Chicago Manual of Style (16th Edition):

Wan, Muting. “Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics.” 2015. Doctoral Dissertation, Cornell University. Accessed November 28, 2020. http://hdl.handle.net/1813/39389.

MLA Handbook (7th Edition):

Wan, Muting. “Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics.” 2015. Web. 28 Nov 2020.

Vancouver:

Wan M. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics. [Internet] [Doctoral dissertation]. Cornell University; 2015. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/1813/39389.

Council of Science Editors:

Wan M. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics. [Doctoral Dissertation]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/39389


Cornell University

12. Kristensen, Jesper. Uncertainty Quantification With Surrogate Models In Alloy Modeling.

Degree: PhD, Applied Physics, 2015, Cornell University

 The success of computational materials science in designing the materials of the future relies on the computation of materials properties using temporally expensive computer codes.… (more)

Subjects/Keywords: Uncertainty quantification; Surrogate models; Bayesian probability theory

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APA (6th Edition):

Kristensen, J. (2015). Uncertainty Quantification With Surrogate Models In Alloy Modeling. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/40658

Chicago Manual of Style (16th Edition):

Kristensen, Jesper. “Uncertainty Quantification With Surrogate Models In Alloy Modeling.” 2015. Doctoral Dissertation, Cornell University. Accessed November 28, 2020. http://hdl.handle.net/1813/40658.

MLA Handbook (7th Edition):

Kristensen, Jesper. “Uncertainty Quantification With Surrogate Models In Alloy Modeling.” 2015. Web. 28 Nov 2020.

Vancouver:

Kristensen J. Uncertainty Quantification With Surrogate Models In Alloy Modeling. [Internet] [Doctoral dissertation]. Cornell University; 2015. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/1813/40658.

Council of Science Editors:

Kristensen J. Uncertainty Quantification With Surrogate Models In Alloy Modeling. [Doctoral Dissertation]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/40658


University of Adelaide

13. Webb, Michael Roy. New methodologies for modelling individual differences in cognition.

Degree: 2010, University of Adelaide

 Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility… (more)

Subjects/Keywords: cognitive models; individual differences; Bayesian graphical modelling

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APA (6th Edition):

Webb, M. R. (2010). New methodologies for modelling individual differences in cognition. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/62779

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):

Webb, Michael Roy. “New methodologies for modelling individual differences in cognition.” 2010. Thesis, University of Adelaide. Accessed November 28, 2020. http://hdl.handle.net/2440/62779.

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

MLA Handbook (7th Edition):

Webb, Michael Roy. “New methodologies for modelling individual differences in cognition.” 2010. Web. 28 Nov 2020.

Vancouver:

Webb MR. New methodologies for modelling individual differences in cognition. [Internet] [Thesis]. University of Adelaide; 2010. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/2440/62779.

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

Council of Science Editors:

Webb MR. New methodologies for modelling individual differences in cognition. [Thesis]. University of Adelaide; 2010. Available from: http://hdl.handle.net/2440/62779

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


University of Plymouth

14. Al-Kaabawi, Zainab A. A. Bayesian hierarchical models for linear networks.

Degree: PhD, 2018, University of Plymouth

 A motorway network is handled as a linear network. The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents… (more)

Subjects/Keywords: Hierarchical models; Bayesian methods; Linear networks

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APA (6th Edition):

Al-Kaabawi, Z. A. A. (2018). Bayesian hierarchical models for linear networks. (Doctoral Dissertation). University of Plymouth. Retrieved from http://hdl.handle.net/10026.1/12829

Chicago Manual of Style (16th Edition):

Al-Kaabawi, Zainab A A. “Bayesian hierarchical models for linear networks.” 2018. Doctoral Dissertation, University of Plymouth. Accessed November 28, 2020. http://hdl.handle.net/10026.1/12829.

MLA Handbook (7th Edition):

Al-Kaabawi, Zainab A A. “Bayesian hierarchical models for linear networks.” 2018. Web. 28 Nov 2020.

Vancouver:

Al-Kaabawi ZAA. Bayesian hierarchical models for linear networks. [Internet] [Doctoral dissertation]. University of Plymouth; 2018. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10026.1/12829.

Council of Science Editors:

Al-Kaabawi ZAA. Bayesian hierarchical models for linear networks. [Doctoral Dissertation]. University of Plymouth; 2018. Available from: http://hdl.handle.net/10026.1/12829


Hong Kong University of Science and Technology

15. Chan, Yun Kwan. Link prediction via ranking with a multiple membership nonparametric Bayesian model.

Degree: 2012, Hong Kong University of Science and Technology

 Link prediction in complex networks has found applications in a wide range of real-world domains involving relational data. The goal is to predict some hidden… (more)

Subjects/Keywords: Bayesian statistical decision theory ; Uncertainty  – Mathematical models

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APA (6th Edition):

Chan, Y. K. (2012). Link prediction via ranking with a multiple membership nonparametric Bayesian model. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-7759 ; https://doi.org/10.14711/thesis-b1198293 ; http://repository.ust.hk/ir/bitstream/1783.1-7759/1/th_redirect.html

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

Chicago Manual of Style (16th Edition):

Chan, Yun Kwan. “Link prediction via ranking with a multiple membership nonparametric Bayesian model.” 2012. Thesis, Hong Kong University of Science and Technology. Accessed November 28, 2020. http://repository.ust.hk/ir/Record/1783.1-7759 ; https://doi.org/10.14711/thesis-b1198293 ; http://repository.ust.hk/ir/bitstream/1783.1-7759/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Chan, Yun Kwan. “Link prediction via ranking with a multiple membership nonparametric Bayesian model.” 2012. Web. 28 Nov 2020.

Vancouver:

Chan YK. Link prediction via ranking with a multiple membership nonparametric Bayesian model. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2012. [cited 2020 Nov 28]. Available from: http://repository.ust.hk/ir/Record/1783.1-7759 ; https://doi.org/10.14711/thesis-b1198293 ; http://repository.ust.hk/ir/bitstream/1783.1-7759/1/th_redirect.html.

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

Council of Science Editors:

Chan YK. Link prediction via ranking with a multiple membership nonparametric Bayesian model. [Thesis]. Hong Kong University of Science and Technology; 2012. Available from: http://repository.ust.hk/ir/Record/1783.1-7759 ; https://doi.org/10.14711/thesis-b1198293 ; http://repository.ust.hk/ir/bitstream/1783.1-7759/1/th_redirect.html

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


Michigan Technological University

16. Feng, Lilia. Bayesian Hypothesis Testing in Linear Regression Models.

Degree: PhD, Department of Mathematical Sciences, 2019, Michigan Technological University

  This dissertation consists of five chapters with three distinct but related research projects. In Chapter 1, we introduce some necessary definitions related to the… (more)

Subjects/Keywords: Bayesian Statistics; Hypothesis Testing; Linear Regression Models

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APA (6th Edition):

Feng, L. (2019). Bayesian Hypothesis Testing in Linear Regression Models. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/928

Chicago Manual of Style (16th Edition):

Feng, Lilia. “Bayesian Hypothesis Testing in Linear Regression Models.” 2019. Doctoral Dissertation, Michigan Technological University. Accessed November 28, 2020. https://digitalcommons.mtu.edu/etdr/928.

MLA Handbook (7th Edition):

Feng, Lilia. “Bayesian Hypothesis Testing in Linear Regression Models.” 2019. Web. 28 Nov 2020.

Vancouver:

Feng L. Bayesian Hypothesis Testing in Linear Regression Models. [Internet] [Doctoral dissertation]. Michigan Technological University; 2019. [cited 2020 Nov 28]. Available from: https://digitalcommons.mtu.edu/etdr/928.

Council of Science Editors:

Feng L. Bayesian Hypothesis Testing in Linear Regression Models. [Doctoral Dissertation]. Michigan Technological University; 2019. Available from: https://digitalcommons.mtu.edu/etdr/928


University of Minnesota

17. Groth, Caroline. Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response.

Degree: PhD, Biostatistics, 2017, University of Minnesota

 In April 2010, the Deepwater Horizon oil rig caught fire and sank, sending approximately 5 million barrels of oil into the Gulf of Mexico over… (more)

Subjects/Keywords: Bayesian; Deepwater Horizon; exposure assessment; linear models

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APA (6th Edition):

Groth, C. (2017). Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206663

Chicago Manual of Style (16th Edition):

Groth, Caroline. “Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response.” 2017. Doctoral Dissertation, University of Minnesota. Accessed November 28, 2020. http://hdl.handle.net/11299/206663.

MLA Handbook (7th Edition):

Groth, Caroline. “Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response.” 2017. Web. 28 Nov 2020.

Vancouver:

Groth C. Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/11299/206663.

Council of Science Editors:

Groth C. Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/206663


University of Edinburgh

18. Caldararu, Silvia. Understanding and predicting global leaf phenology using satellite observations of vegetation.

Degree: PhD, 2013, University of Edinburgh

 Leaf phenology refers to the timing of leaf life cycle events and is essential to our understanding of the earth system as it impacts the… (more)

Subjects/Keywords: 578.4; phenology; global vegetation models; Bayesian methods

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APA (6th Edition):

Caldararu, S. (2013). Understanding and predicting global leaf phenology using satellite observations of vegetation. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/7627

Chicago Manual of Style (16th Edition):

Caldararu, Silvia. “Understanding and predicting global leaf phenology using satellite observations of vegetation.” 2013. Doctoral Dissertation, University of Edinburgh. Accessed November 28, 2020. http://hdl.handle.net/1842/7627.

MLA Handbook (7th Edition):

Caldararu, Silvia. “Understanding and predicting global leaf phenology using satellite observations of vegetation.” 2013. Web. 28 Nov 2020.

Vancouver:

Caldararu S. Understanding and predicting global leaf phenology using satellite observations of vegetation. [Internet] [Doctoral dissertation]. University of Edinburgh; 2013. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/1842/7627.

Council of Science Editors:

Caldararu S. Understanding and predicting global leaf phenology using satellite observations of vegetation. [Doctoral Dissertation]. University of Edinburgh; 2013. Available from: http://hdl.handle.net/1842/7627


Universitat de Valencia

19. Martínez Minaya, Joaquín. Recent statistical advances and applications of species distribution modeling .

Degree: 2019, Universitat de Valencia

 En el mundo en que vivimos, producimos aproximadamente 2.5 quintillones de bytes de datos por día. Esta enorme cantidad de datos proviene de las redes… (more)

Subjects/Keywords: bayesian inference; inla; species distribution models; geostatistics

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APA (6th Edition):

Martínez Minaya, J. (2019). Recent statistical advances and applications of species distribution modeling . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/71315

Chicago Manual of Style (16th Edition):

Martínez Minaya, Joaquín. “Recent statistical advances and applications of species distribution modeling .” 2019. Doctoral Dissertation, Universitat de Valencia. Accessed November 28, 2020. http://hdl.handle.net/10550/71315.

MLA Handbook (7th Edition):

Martínez Minaya, Joaquín. “Recent statistical advances and applications of species distribution modeling .” 2019. Web. 28 Nov 2020.

Vancouver:

Martínez Minaya J. Recent statistical advances and applications of species distribution modeling . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2019. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10550/71315.

Council of Science Editors:

Martínez Minaya J. Recent statistical advances and applications of species distribution modeling . [Doctoral Dissertation]. Universitat de Valencia; 2019. Available from: http://hdl.handle.net/10550/71315

20. Bratières, Sébastien. Non-parametric Bayesian models for structured output prediction.

Degree: PhD, 2018, University of Cambridge

 Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple,… (more)

Subjects/Keywords: 006.3; machine learning; Bayesian models; probability

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APA (6th Edition):

Bratières, S. (2018). Non-parametric Bayesian models for structured output prediction. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.22124 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744725

Chicago Manual of Style (16th Edition):

Bratières, Sébastien. “Non-parametric Bayesian models for structured output prediction.” 2018. Doctoral Dissertation, University of Cambridge. Accessed November 28, 2020. https://doi.org/10.17863/CAM.22124 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744725.

MLA Handbook (7th Edition):

Bratières, Sébastien. “Non-parametric Bayesian models for structured output prediction.” 2018. Web. 28 Nov 2020.

Vancouver:

Bratières S. Non-parametric Bayesian models for structured output prediction. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2020 Nov 28]. Available from: https://doi.org/10.17863/CAM.22124 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744725.

Council of Science Editors:

Bratières S. Non-parametric Bayesian models for structured output prediction. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://doi.org/10.17863/CAM.22124 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744725


Virginia Tech

21. Metzger, Thomas Anthony. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.

Degree: PhD, Statistics, 2019, Virginia Tech

 Statistical models are a powerful tool for describing a broad range of phenomena in our world. However, many common statistical models may make assumptions that… (more)

Subjects/Keywords: model selection; heteroscedasticity; linear models; Bayesian

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APA (6th Edition):

Metzger, T. A. (2019). Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93226

Chicago Manual of Style (16th Edition):

Metzger, Thomas Anthony. “Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.” 2019. Doctoral Dissertation, Virginia Tech. Accessed November 28, 2020. http://hdl.handle.net/10919/93226.

MLA Handbook (7th Edition):

Metzger, Thomas Anthony. “Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.” 2019. Web. 28 Nov 2020.

Vancouver:

Metzger TA. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10919/93226.

Council of Science Editors:

Metzger TA. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93226


University of New South Wales

22. Gan, Quan. Topics in econometrics.

Degree: Economics, 2013, University of New South Wales

 Multivariate data modelling is an important and growing area of econometrics. There are two general approaches of modelling multivariate data: 1) modelling margins and copula… (more)

Subjects/Keywords: Skew-t Distribution; Bayesian methods; Factor models

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APA (6th Edition):

Gan, Q. (2013). Topics in econometrics. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53308 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12003/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Gan, Quan. “Topics in econometrics.” 2013. Doctoral Dissertation, University of New South Wales. Accessed November 28, 2020. http://handle.unsw.edu.au/1959.4/53308 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12003/SOURCE02?view=true.

MLA Handbook (7th Edition):

Gan, Quan. “Topics in econometrics.” 2013. Web. 28 Nov 2020.

Vancouver:

Gan Q. Topics in econometrics. [Internet] [Doctoral dissertation]. University of New South Wales; 2013. [cited 2020 Nov 28]. Available from: http://handle.unsw.edu.au/1959.4/53308 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12003/SOURCE02?view=true.

Council of Science Editors:

Gan Q. Topics in econometrics. [Doctoral Dissertation]. University of New South Wales; 2013. Available from: http://handle.unsw.edu.au/1959.4/53308 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12003/SOURCE02?view=true


Rutgers University

23. Wiedenhoeft, John. Dynamically compressed Bayesian hidden Markov models using Haar wavelets.

Degree: PhD, Computer Science, 2018, Rutgers University

 Hidden Markov Models (HMM) are a powerful and ubiquitous tool for segmentation and labeling in bioinformatics and beyond. Classic techniques to infer suitable model parameters… (more)

Subjects/Keywords: Bayesian field theory; Hidden Markov models

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APA (6th Edition):

Wiedenhoeft, J. (2018). Dynamically compressed Bayesian hidden Markov models using Haar wavelets. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/59275/

Chicago Manual of Style (16th Edition):

Wiedenhoeft, John. “Dynamically compressed Bayesian hidden Markov models using Haar wavelets.” 2018. Doctoral Dissertation, Rutgers University. Accessed November 28, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/59275/.

MLA Handbook (7th Edition):

Wiedenhoeft, John. “Dynamically compressed Bayesian hidden Markov models using Haar wavelets.” 2018. Web. 28 Nov 2020.

Vancouver:

Wiedenhoeft J. Dynamically compressed Bayesian hidden Markov models using Haar wavelets. [Internet] [Doctoral dissertation]. Rutgers University; 2018. [cited 2020 Nov 28]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/59275/.

Council of Science Editors:

Wiedenhoeft J. Dynamically compressed Bayesian hidden Markov models using Haar wavelets. [Doctoral Dissertation]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/59275/


Texas A&M University

24. Talluri, Rajesh. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.

Degree: PhD, Statistics, 2012, Texas A&M University

 We propose Bayesian methods for estimating the precision matrix in Gaussian graphical models. The methods lead to sparse and adaptively shrunk estimators of the precision… (more)

Subjects/Keywords: Bayesian; Gaussian Graphical Models; Covariance Selection; Mixture Models

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APA (6th Edition):

Talluri, R. (2012). Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828

Chicago Manual of Style (16th Edition):

Talluri, Rajesh. “Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.” 2012. Doctoral Dissertation, Texas A&M University. Accessed November 28, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828.

MLA Handbook (7th Edition):

Talluri, Rajesh. “Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.” 2012. Web. 28 Nov 2020.

Vancouver:

Talluri R. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. [Internet] [Doctoral dissertation]. Texas A&M University; 2012. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828.

Council of Science Editors:

Talluri R. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. [Doctoral Dissertation]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828


Universitat de Valencia

25. Sarzo Carles, Blanca. New insights in Bayesian Survival Analysis in Ecology .

Degree: 2020, Universitat de Valencia

 La fauna silvestre está asediada. Y ésta no es solo una frase impactante con la que empezar una tesis, tristemente, es una realidad. En el… (more)

Subjects/Keywords: capture-recapture models; integrated models; bayesian inference; survival analysis; statistical Ecology

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APA (6th Edition):

Sarzo Carles, B. (2020). New insights in Bayesian Survival Analysis in Ecology . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/75252

Chicago Manual of Style (16th Edition):

Sarzo Carles, Blanca. “New insights in Bayesian Survival Analysis in Ecology .” 2020. Doctoral Dissertation, Universitat de Valencia. Accessed November 28, 2020. http://hdl.handle.net/10550/75252.

MLA Handbook (7th Edition):

Sarzo Carles, Blanca. “New insights in Bayesian Survival Analysis in Ecology .” 2020. Web. 28 Nov 2020.

Vancouver:

Sarzo Carles B. New insights in Bayesian Survival Analysis in Ecology . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2020. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10550/75252.

Council of Science Editors:

Sarzo Carles B. New insights in Bayesian Survival Analysis in Ecology . [Doctoral Dissertation]. Universitat de Valencia; 2020. Available from: http://hdl.handle.net/10550/75252


University of Houston

26. Liutec, Carmen M. A Multi-Product Individual-Level Model for New Product Sales: Forecasting and Insights.

Degree: PhD, Marketing, University of Houston

 We develop a novel approach for modeling new product trial and early repeat purchase behavior, and we apply this approach in the context of consumer… (more)

Subjects/Keywords: New product models; Bayesian models

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APA (6th Edition):

Liutec, C. M. (n.d.). A Multi-Product Individual-Level Model for New Product Sales: Forecasting and Insights. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/2783

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Chicago Manual of Style (16th Edition):

Liutec, Carmen M. “A Multi-Product Individual-Level Model for New Product Sales: Forecasting and Insights.” Doctoral Dissertation, University of Houston. Accessed November 28, 2020. http://hdl.handle.net/10657/2783.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

MLA Handbook (7th Edition):

Liutec, Carmen M. “A Multi-Product Individual-Level Model for New Product Sales: Forecasting and Insights.” Web. 28 Nov 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Liutec CM. A Multi-Product Individual-Level Model for New Product Sales: Forecasting and Insights. [Internet] [Doctoral dissertation]. University of Houston; [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10657/2783.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Council of Science Editors:

Liutec CM. A Multi-Product Individual-Level Model for New Product Sales: Forecasting and Insights. [Doctoral Dissertation]. University of Houston; Available from: http://hdl.handle.net/10657/2783

Note: this citation may be lacking information needed for this citation format:
No year of publication.


University of Oxford

27. Harrison, Jonathan U. Developing and applying modelling and Bayesian inference tools for developmental biology.

Degree: PhD, 2018, University of Oxford

 Developmental biology allows us to answer crucial questions about how patterned, polarized cells can organize robustly and repeatably to form living tissues and organisms. Quantitative… (more)

Subjects/Keywords: Bayesian statistics; Developmental biology  – Mathematical models; Biology – Mathematical models

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APA (6th Edition):

Harrison, J. U. (2018). Developing and applying modelling and Bayesian inference tools for developmental biology. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:1528bb66-a03a-431f-8a8d-4a7bd25a9843 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.780644

Chicago Manual of Style (16th Edition):

Harrison, Jonathan U. “Developing and applying modelling and Bayesian inference tools for developmental biology.” 2018. Doctoral Dissertation, University of Oxford. Accessed November 28, 2020. http://ora.ox.ac.uk/objects/uuid:1528bb66-a03a-431f-8a8d-4a7bd25a9843 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.780644.

MLA Handbook (7th Edition):

Harrison, Jonathan U. “Developing and applying modelling and Bayesian inference tools for developmental biology.” 2018. Web. 28 Nov 2020.

Vancouver:

Harrison JU. Developing and applying modelling and Bayesian inference tools for developmental biology. [Internet] [Doctoral dissertation]. University of Oxford; 2018. [cited 2020 Nov 28]. Available from: http://ora.ox.ac.uk/objects/uuid:1528bb66-a03a-431f-8a8d-4a7bd25a9843 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.780644.

Council of Science Editors:

Harrison JU. Developing and applying modelling and Bayesian inference tools for developmental biology. [Doctoral Dissertation]. University of Oxford; 2018. Available from: http://ora.ox.ac.uk/objects/uuid:1528bb66-a03a-431f-8a8d-4a7bd25a9843 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.780644


University of Florida

28. Jalali, Peyman. Bayesian Estimation and Model Selection for Single and Multiple Graphical Models.

Degree: PhD, Statistics, 2019, University of Florida

 Undirected Graphical Models represent a family of canonical statistical models for reconstructing interactions amongst a set of entities from multi-dimensional data profiles. They have numerous… (more)

Subjects/Keywords: bayesian-graphical-models  – joint-estimation-of-graphical-models  – metabolomics-data

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APA (6th Edition):

Jalali, P. (2019). Bayesian Estimation and Model Selection for Single and Multiple Graphical Models. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0054270

Chicago Manual of Style (16th Edition):

Jalali, Peyman. “Bayesian Estimation and Model Selection for Single and Multiple Graphical Models.” 2019. Doctoral Dissertation, University of Florida. Accessed November 28, 2020. https://ufdc.ufl.edu/UFE0054270.

MLA Handbook (7th Edition):

Jalali, Peyman. “Bayesian Estimation and Model Selection for Single and Multiple Graphical Models.” 2019. Web. 28 Nov 2020.

Vancouver:

Jalali P. Bayesian Estimation and Model Selection for Single and Multiple Graphical Models. [Internet] [Doctoral dissertation]. University of Florida; 2019. [cited 2020 Nov 28]. Available from: https://ufdc.ufl.edu/UFE0054270.

Council of Science Editors:

Jalali P. Bayesian Estimation and Model Selection for Single and Multiple Graphical Models. [Doctoral Dissertation]. University of Florida; 2019. Available from: https://ufdc.ufl.edu/UFE0054270

29. Paniw Simpson, Maria. Demografía y ecología evolutiva del subarbusto carnívoro Drosophyllum lusitanicum (L.) Link. (Drosophyllaceae): Demography and Evolutionary Ecology of the Carnivorous Subshrub Drosophyllum lusitanicum (L.) Link (Drosophyllaceae).

Degree: 2016, Universidad de Cádiz

 Natural disturbances occur in various ecosystems and have resulted in the evolution of life histories to buffer or even benefit from disturbance regimes. However, human… (more)

Subjects/Keywords: conservation; demography; integral projection models; Bayesian models; plant carnivory

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APA (6th Edition):

Paniw Simpson, M. (2016). Demografía y ecología evolutiva del subarbusto carnívoro Drosophyllum lusitanicum (L.) Link. (Drosophyllaceae): Demography and Evolutionary Ecology of the Carnivorous Subshrub Drosophyllum lusitanicum (L.) Link (Drosophyllaceae). (Thesis). Universidad de Cádiz. Retrieved from http://hdl.handle.net/10498/19821

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):

Paniw Simpson, Maria. “Demografía y ecología evolutiva del subarbusto carnívoro Drosophyllum lusitanicum (L.) Link. (Drosophyllaceae): Demography and Evolutionary Ecology of the Carnivorous Subshrub Drosophyllum lusitanicum (L.) Link (Drosophyllaceae).” 2016. Thesis, Universidad de Cádiz. Accessed November 28, 2020. http://hdl.handle.net/10498/19821.

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

MLA Handbook (7th Edition):

Paniw Simpson, Maria. “Demografía y ecología evolutiva del subarbusto carnívoro Drosophyllum lusitanicum (L.) Link. (Drosophyllaceae): Demography and Evolutionary Ecology of the Carnivorous Subshrub Drosophyllum lusitanicum (L.) Link (Drosophyllaceae).” 2016. Web. 28 Nov 2020.

Vancouver:

Paniw Simpson M. Demografía y ecología evolutiva del subarbusto carnívoro Drosophyllum lusitanicum (L.) Link. (Drosophyllaceae): Demography and Evolutionary Ecology of the Carnivorous Subshrub Drosophyllum lusitanicum (L.) Link (Drosophyllaceae). [Internet] [Thesis]. Universidad de Cádiz; 2016. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10498/19821.

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

Council of Science Editors:

Paniw Simpson M. Demografía y ecología evolutiva del subarbusto carnívoro Drosophyllum lusitanicum (L.) Link. (Drosophyllaceae): Demography and Evolutionary Ecology of the Carnivorous Subshrub Drosophyllum lusitanicum (L.) Link (Drosophyllaceae). [Thesis]. Universidad de Cádiz; 2016. Available from: http://hdl.handle.net/10498/19821

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


Universitat de Valencia

30. Amorós Salvador, Rubén. Bayesian temporal and spatio-temporal Markov switching models for the detection of influenza outbreaks .

Degree: 2017, Universitat de Valencia

 Influenza is a disease which affects millions of people every year and causes hundreds of thousends of deads every year. This disease causes substantial direct… (more)

Subjects/Keywords: outbreaks detection; markov switching models; influenza; bayesian; spatio-temporal models

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APA (6th Edition):

Amorós Salvador, R. (2017). Bayesian temporal and spatio-temporal Markov switching models for the detection of influenza outbreaks . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/59265

Chicago Manual of Style (16th Edition):

Amorós Salvador, Rubén. “Bayesian temporal and spatio-temporal Markov switching models for the detection of influenza outbreaks .” 2017. Doctoral Dissertation, Universitat de Valencia. Accessed November 28, 2020. http://hdl.handle.net/10550/59265.

MLA Handbook (7th Edition):

Amorós Salvador, Rubén. “Bayesian temporal and spatio-temporal Markov switching models for the detection of influenza outbreaks .” 2017. Web. 28 Nov 2020.

Vancouver:

Amorós Salvador R. Bayesian temporal and spatio-temporal Markov switching models for the detection of influenza outbreaks . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2017. [cited 2020 Nov 28]. Available from: http://hdl.handle.net/10550/59265.

Council of Science Editors:

Amorós Salvador R. Bayesian temporal and spatio-temporal Markov switching models for the detection of influenza outbreaks . [Doctoral Dissertation]. Universitat de Valencia; 2017. Available from: http://hdl.handle.net/10550/59265

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