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ETH Zürich

1. Gotovos, Alkis. Sampling from Probabilistic Submodular Models.

Degree: 2019, ETH Zürich

URL: http://hdl.handle.net/20.500.11850/333042

► Practical problems of discrete nature are very common in machine learning; application domains include computer vision (e.g., image segmentation), sequential decision making (e.g., active learning),…
(more)

Subjects/Keywords: Approximate inference; Probabilistic models; Sampling; Submodularity

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

APA (6^{th} Edition):

Gotovos, A. (2019). Sampling from Probabilistic Submodular Models. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/333042

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

Gotovos, Alkis. “Sampling from Probabilistic Submodular Models.” 2019. Doctoral Dissertation, ETH Zürich. Accessed December 07, 2019. http://hdl.handle.net/20.500.11850/333042.

MLA Handbook (7^{th} Edition):

Gotovos, Alkis. “Sampling from Probabilistic Submodular Models.” 2019. Web. 07 Dec 2019.

Vancouver:

Gotovos A. Sampling from Probabilistic Submodular Models. [Internet] [Doctoral dissertation]. ETH Zürich; 2019. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/20.500.11850/333042.

Council of Science Editors:

Gotovos A. Sampling from Probabilistic Submodular Models. [Doctoral Dissertation]. ETH Zürich; 2019. Available from: http://hdl.handle.net/20.500.11850/333042

University of Cambridge

2.
Hennig, Philipp.
*Approximate**inference* in graphical models.

Degree: PhD, 2011, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/237251 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854

► Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertainty, allowing the construction of complex hierarchical models for real-world *inference* tasks. Unfortunately, exact…
(more)

Subjects/Keywords: 519.2; Applied mathematics; Computer science; Probability theory; Probabilistic inference; Graphical models; Approximate inference

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

APA (6^{th} Edition):

Hennig, P. (2011). Approximate inference in graphical models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/237251 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854

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

Hennig, Philipp. “Approximate inference in graphical models.” 2011. Doctoral Dissertation, University of Cambridge. Accessed December 07, 2019. https://www.repository.cam.ac.uk/handle/1810/237251 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854.

MLA Handbook (7^{th} Edition):

Hennig, Philipp. “Approximate inference in graphical models.” 2011. Web. 07 Dec 2019.

Vancouver:

Hennig P. Approximate inference in graphical models. [Internet] [Doctoral dissertation]. University of Cambridge; 2011. [cited 2019 Dec 07]. Available from: https://www.repository.cam.ac.uk/handle/1810/237251 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854.

Council of Science Editors:

Hennig P. Approximate inference in graphical models. [Doctoral Dissertation]. University of Cambridge; 2011. Available from: https://www.repository.cam.ac.uk/handle/1810/237251 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854

University of Cambridge

3.
Hennig, Philipp.
*Approximate**inference* in graphical models
.

Degree: 2011, University of Cambridge

URL: http://www.dspace.cam.ac.uk/handle/1810/237251

► Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertainty, allowing the construction of complex hierarchical models for real-world *inference* tasks. Unfortunately, exact…
(more)

Subjects/Keywords: Applied mathematics; Computer science; Probability theory; Probabilistic inference; Graphical models; Approximate inference

Record Details Similar Records

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

APA (6^{th} Edition):

Hennig, P. (2011). Approximate inference in graphical models . (Thesis). University of Cambridge. Retrieved from http://www.dspace.cam.ac.uk/handle/1810/237251

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

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

Hennig, Philipp. “Approximate inference in graphical models .” 2011. Thesis, University of Cambridge. Accessed December 07, 2019. http://www.dspace.cam.ac.uk/handle/1810/237251.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hennig, Philipp. “Approximate inference in graphical models .” 2011. Web. 07 Dec 2019.

Vancouver:

Hennig P. Approximate inference in graphical models . [Internet] [Thesis]. University of Cambridge; 2011. [cited 2019 Dec 07]. Available from: http://www.dspace.cam.ac.uk/handle/1810/237251.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hennig P. Approximate inference in graphical models . [Thesis]. University of Cambridge; 2011. Available from: http://www.dspace.cam.ac.uk/handle/1810/237251

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Irvine

4.
Lou, Qi.
Anytime *Approximate* *Inference* in Graphical Models.

Degree: Computer Science, 2018, University of California – Irvine

URL: http://www.escholarship.org/uc/item/7sc0m97f

► Graphical models are a powerful framework for modeling interactions within complex systems. Reasoning over graphical models typically involves answering *inference* queries, such as computing the…
(more)

Subjects/Keywords: Artificial intelligence; anyspace; anytime; approximate inference; graphical models

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

APA (6^{th} Edition):

Lou, Q. (2018). Anytime Approximate Inference in Graphical Models. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/7sc0m97f

Not specified: Masters Thesis or Doctoral Dissertation

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

Lou, Qi. “Anytime Approximate Inference in Graphical Models.” 2018. Thesis, University of California – Irvine. Accessed December 07, 2019. http://www.escholarship.org/uc/item/7sc0m97f.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lou, Qi. “Anytime Approximate Inference in Graphical Models.” 2018. Web. 07 Dec 2019.

Vancouver:

Lou Q. Anytime Approximate Inference in Graphical Models. [Internet] [Thesis]. University of California – Irvine; 2018. [cited 2019 Dec 07]. Available from: http://www.escholarship.org/uc/item/7sc0m97f.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lou Q. Anytime Approximate Inference in Graphical Models. [Thesis]. University of California – Irvine; 2018. Available from: http://www.escholarship.org/uc/item/7sc0m97f

Not specified: Masters Thesis or Doctoral Dissertation

University of Wollongong

5. Neville, Sarah Elizabeth. Elaborate distribution semiparametric regression via mean field variational Bayes.

Degree: PhD, 2013, University of Wollongong

URL: 0103 NUMERICAL AND COMPUTATIONAL MATHEMATICS, 0104 STATISTICS ; https://ro.uow.edu.au/theses/3958

► Mean field variational Bayes (MFVB) is a fast, deterministic *inference* tool for use in Bayesian hierarchical models. We develop and examine the performance of…
(more)

Subjects/Keywords: approximate Bayesian inference; variational approximation; semiparametric regression; continued fraction

Record Details Similar Records

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

APA (6^{th} Edition):

Neville, S. E. (2013). Elaborate distribution semiparametric regression via mean field variational Bayes. (Doctoral Dissertation). University of Wollongong. Retrieved from 0103 NUMERICAL AND COMPUTATIONAL MATHEMATICS, 0104 STATISTICS ; https://ro.uow.edu.au/theses/3958

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

Neville, Sarah Elizabeth. “Elaborate distribution semiparametric regression via mean field variational Bayes.” 2013. Doctoral Dissertation, University of Wollongong. Accessed December 07, 2019. 0103 NUMERICAL AND COMPUTATIONAL MATHEMATICS, 0104 STATISTICS ; https://ro.uow.edu.au/theses/3958.

MLA Handbook (7^{th} Edition):

Neville, Sarah Elizabeth. “Elaborate distribution semiparametric regression via mean field variational Bayes.” 2013. Web. 07 Dec 2019.

Vancouver:

Neville SE. Elaborate distribution semiparametric regression via mean field variational Bayes. [Internet] [Doctoral dissertation]. University of Wollongong; 2013. [cited 2019 Dec 07]. Available from: 0103 NUMERICAL AND COMPUTATIONAL MATHEMATICS, 0104 STATISTICS ; https://ro.uow.edu.au/theses/3958.

Council of Science Editors:

Neville SE. Elaborate distribution semiparametric regression via mean field variational Bayes. [Doctoral Dissertation]. University of Wollongong; 2013. Available from: 0103 NUMERICAL AND COMPUTATIONAL MATHEMATICS, 0104 STATISTICS ; https://ro.uow.edu.au/theses/3958

University of Toronto

6.
Dhoot, Aditya.
Wind Farm Layout Optimization Using *Approximate* *Inference* in Graphical Models.

Degree: 2016, University of Toronto

URL: http://hdl.handle.net/1807/72692

►

Wind farm layout optimization (WFLO) determines the optimal location of wind turbines within a fixed geographical area to maximize the total power capacity of the… (more)

Subjects/Keywords: approximate inference; probabilistic graphical models; wind farm design; 0796

Record Details Similar Records

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

APA (6^{th} Edition):

Dhoot, A. (2016). Wind Farm Layout Optimization Using Approximate Inference in Graphical Models. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/72692

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

Dhoot, Aditya. “Wind Farm Layout Optimization Using Approximate Inference in Graphical Models.” 2016. Masters Thesis, University of Toronto. Accessed December 07, 2019. http://hdl.handle.net/1807/72692.

MLA Handbook (7^{th} Edition):

Dhoot, Aditya. “Wind Farm Layout Optimization Using Approximate Inference in Graphical Models.” 2016. Web. 07 Dec 2019.

Vancouver:

Dhoot A. Wind Farm Layout Optimization Using Approximate Inference in Graphical Models. [Internet] [Masters thesis]. University of Toronto; 2016. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/1807/72692.

Council of Science Editors:

Dhoot A. Wind Farm Layout Optimization Using Approximate Inference in Graphical Models. [Masters Thesis]. University of Toronto; 2016. Available from: http://hdl.handle.net/1807/72692

University of Southern California

7.
Alaghband, Mohammad-Reza.
* Inference* for stochastic models of molecular data.

Degree: PhD, Applied Mathematics, 2009, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/517465/rec/3470

► This dissertation consists of three chapters. The chapters are bound together, inprinciple, by the discussed statistical *inference* methods for understanding different types of molecular data.…
(more)

Subjects/Keywords: projection pursuit; discrete data; approximate Bayesian computation; inference; biomarker; ROC

Record Details Similar Records

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

APA (6^{th} Edition):

Alaghband, M. (2009). Inference for stochastic models of molecular data. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/517465/rec/3470

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

Alaghband, Mohammad-Reza. “Inference for stochastic models of molecular data.” 2009. Doctoral Dissertation, University of Southern California. Accessed December 07, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/517465/rec/3470.

MLA Handbook (7^{th} Edition):

Alaghband, Mohammad-Reza. “Inference for stochastic models of molecular data.” 2009. Web. 07 Dec 2019.

Vancouver:

Alaghband M. Inference for stochastic models of molecular data. [Internet] [Doctoral dissertation]. University of Southern California; 2009. [cited 2019 Dec 07]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/517465/rec/3470.

Council of Science Editors:

Alaghband M. Inference for stochastic models of molecular data. [Doctoral Dissertation]. University of Southern California; 2009. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/517465/rec/3470

University of New South Wales

8. Wang, Yi. Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness.

Degree: Computer Science & Engineering, 2016, University of New South Wales

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

► In this thesis, we emphasise on three types of Bayesian nonparametric probabilisticclustering problem: the class-based clustering, the feature-based clustering and theco-clustering. The mapping relationship between…
(more)

Subjects/Keywords: Approximate Inference; Bayesian Nonparametric; MCMC; Stochastic Block Model

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

APA (6^{th} Edition):

Wang, Y. (2016). Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true

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

Wang, Yi. “Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness.” 2016. Doctoral Dissertation, University of New South Wales. Accessed December 07, 2019. http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true.

MLA Handbook (7^{th} Edition):

Wang, Yi. “Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness.” 2016. Web. 07 Dec 2019.

Vancouver:

Wang Y. Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Dec 07]. Available from: http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true.

Council of Science Editors:

Wang Y. Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true

9.
Heard, Daniel Philip.
Statistical *Inference* Utilizing Agent Based Models
.

Degree: 2014, Duke University

URL: http://hdl.handle.net/10161/8687

► Agent-based models (ABMs) are computational models used to simulate the behaviors, actionsand interactions of agents within a system. The individual agents each have their…
(more)

Subjects/Keywords: Statistics; Agent Based Models; Approximate Bayesian Computation; Emulators; Inference

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

APA (6^{th} Edition):

Heard, D. P. (2014). Statistical Inference Utilizing Agent Based Models . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/8687

Not specified: Masters Thesis or Doctoral Dissertation

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

Heard, Daniel Philip. “Statistical Inference Utilizing Agent Based Models .” 2014. Thesis, Duke University. Accessed December 07, 2019. http://hdl.handle.net/10161/8687.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Heard, Daniel Philip. “Statistical Inference Utilizing Agent Based Models .” 2014. Web. 07 Dec 2019.

Vancouver:

Heard DP. Statistical Inference Utilizing Agent Based Models . [Internet] [Thesis]. Duke University; 2014. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10161/8687.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Heard DP. Statistical Inference Utilizing Agent Based Models . [Thesis]. Duke University; 2014. Available from: http://hdl.handle.net/10161/8687

Not specified: Masters Thesis or Doctoral Dissertation

Duke University

10.
Tan, Zilong.
*Approximate**Inference* for High-Dimensional Latent Variable Models
.

Degree: 2018, Duke University

URL: http://hdl.handle.net/10161/18280

► Latent variable models are widely used in applications ranging from natural language processing to recommender systems. Exact *inference* using maximum likelihood for these models…
(more)

Subjects/Keywords: Computer science; approximate inference; latent variable models; machine learning

Record Details Similar Records

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

APA (6^{th} Edition):

Tan, Z. (2018). Approximate Inference for High-Dimensional Latent Variable Models . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/18280

Not specified: Masters Thesis or Doctoral Dissertation

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

Tan, Zilong. “Approximate Inference for High-Dimensional Latent Variable Models .” 2018. Thesis, Duke University. Accessed December 07, 2019. http://hdl.handle.net/10161/18280.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Tan, Zilong. “Approximate Inference for High-Dimensional Latent Variable Models .” 2018. Web. 07 Dec 2019.

Vancouver:

Tan Z. Approximate Inference for High-Dimensional Latent Variable Models . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10161/18280.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tan Z. Approximate Inference for High-Dimensional Latent Variable Models . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/18280

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Irvine

11. Liu, Qiang. Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework.

Degree: Computer Science, 2014, University of California – Irvine

URL: http://www.escholarship.org/uc/item/92p8w3xb

► Probabilistic graphical models such as Markov random fields, Bayesian networks and decision networks (a.k.a. influence diagrams) provide powerful frameworks for representing and exploiting dependence structures…
(more)

Subjects/Keywords: Computer science; Artificial intelligence; Approximate inference; Belief propagation; Graphical models; Influence diagrams; Probabilistic inference; Variational methods

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

APA (6^{th} Edition):

Liu, Q. (2014). Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/92p8w3xb

Not specified: Masters Thesis or Doctoral Dissertation

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

Liu, Qiang. “Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework.” 2014. Thesis, University of California – Irvine. Accessed December 07, 2019. http://www.escholarship.org/uc/item/92p8w3xb.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Liu, Qiang. “Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework.” 2014. Web. 07 Dec 2019.

Vancouver:

Liu Q. Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. [Internet] [Thesis]. University of California – Irvine; 2014. [cited 2019 Dec 07]. Available from: http://www.escholarship.org/uc/item/92p8w3xb.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu Q. Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. [Thesis]. University of California – Irvine; 2014. Available from: http://www.escholarship.org/uc/item/92p8w3xb

Not specified: Masters Thesis or Doctoral Dissertation

University of Oxford

12. Daly, Aidan C. Statistical tools and community resources for developing trusted models in biology and chemistry.

Degree: PhD, 2017, University of Oxford

URL: https://ora.ox.ac.uk/objects/uuid:4e936e3b-7985-44f0-814c-7be3433bdcbb ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.735959

► Mathematical modeling has been instrumental to the development of natural sciences over the last half-century. Through iterated interactions between modeling and real-world exper- imentation, these…
(more)

Subjects/Keywords: Computational biology; Reproducibility; Bayesian inference; Approximate Bayesian computation; Cardiac cell modeling; Model identifiability

Record Details Similar Records

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

APA (6^{th} Edition):

Daly, A. C. (2017). Statistical tools and community resources for developing trusted models in biology and chemistry. (Doctoral Dissertation). University of Oxford. Retrieved from https://ora.ox.ac.uk/objects/uuid:4e936e3b-7985-44f0-814c-7be3433bdcbb ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.735959

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

Daly, Aidan C. “Statistical tools and community resources for developing trusted models in biology and chemistry.” 2017. Doctoral Dissertation, University of Oxford. Accessed December 07, 2019. https://ora.ox.ac.uk/objects/uuid:4e936e3b-7985-44f0-814c-7be3433bdcbb ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.735959.

MLA Handbook (7^{th} Edition):

Daly, Aidan C. “Statistical tools and community resources for developing trusted models in biology and chemistry.” 2017. Web. 07 Dec 2019.

Vancouver:

Daly AC. Statistical tools and community resources for developing trusted models in biology and chemistry. [Internet] [Doctoral dissertation]. University of Oxford; 2017. [cited 2019 Dec 07]. Available from: https://ora.ox.ac.uk/objects/uuid:4e936e3b-7985-44f0-814c-7be3433bdcbb ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.735959.

Council of Science Editors:

Daly AC. Statistical tools and community resources for developing trusted models in biology and chemistry. [Doctoral Dissertation]. University of Oxford; 2017. Available from: https://ora.ox.ac.uk/objects/uuid:4e936e3b-7985-44f0-814c-7be3433bdcbb ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.735959

University of Adelaide

13. Bruce-Doust, Riley. Forgetting properties of finite-state reciprocal processes.

Degree: 2017, University of Adelaide

URL: http://hdl.handle.net/2440/113380

► Reciprocal chains (RC) are a class of discrete-index, finite-state stochastic process having the non-causal generalisation of the Markov property, where rather than the future being…
(more)

Subjects/Keywords: finite state systems; hidden Markov models (HMMs); Markov processes; approximate inference; reciprocal processes (RP)

Record Details Similar Records

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

APA (6^{th} Edition):

Bruce-Doust, R. (2017). Forgetting properties of finite-state reciprocal processes. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/113380

Not specified: Masters Thesis or Doctoral Dissertation

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

Bruce-Doust, Riley. “Forgetting properties of finite-state reciprocal processes.” 2017. Thesis, University of Adelaide. Accessed December 07, 2019. http://hdl.handle.net/2440/113380.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bruce-Doust, Riley. “Forgetting properties of finite-state reciprocal processes.” 2017. Web. 07 Dec 2019.

Vancouver:

Bruce-Doust R. Forgetting properties of finite-state reciprocal processes. [Internet] [Thesis]. University of Adelaide; 2017. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/2440/113380.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bruce-Doust R. Forgetting properties of finite-state reciprocal processes. [Thesis]. University of Adelaide; 2017. Available from: http://hdl.handle.net/2440/113380

Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

14.
Bui, Thang Duc.
Efficient deterministic *approximate* Bayesian *inference* for Gaussian process models.

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600

► Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian…
(more)

Subjects/Keywords: machine learning; Gaussian process; approximate inference; Bayesian statistics; supervised learning; unsupervised learning

Record Details Similar Records

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

APA (6^{th} Edition):

Bui, T. D. (2018). Efficient deterministic approximate Bayesian inference for Gaussian process models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600

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

Bui, Thang Duc. “Efficient deterministic approximate Bayesian inference for Gaussian process models.” 2018. Doctoral Dissertation, University of Cambridge. Accessed December 07, 2019. https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600.

MLA Handbook (7^{th} Edition):

Bui, Thang Duc. “Efficient deterministic approximate Bayesian inference for Gaussian process models.” 2018. Web. 07 Dec 2019.

Vancouver:

Bui TD. Efficient deterministic approximate Bayesian inference for Gaussian process models. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Dec 07]. Available from: https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600.

Council of Science Editors:

Bui TD. Efficient deterministic approximate Bayesian inference for Gaussian process models. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600

Stellenbosch University

15.
Verrezen, Dylan.
Recommender systems with Bayesian aspect models and the effect of *approximate* * inference*.

Degree: MScEng, Electrical and Electronic Engineering, 2018, Stellenbosch University

URL: http://hdl.handle.net/10019.1/103789

►

ENGLISH ABSTRACT: Recommender systems form an important part of the modern world. These systems allow users to find relevant items in often huge item collections.… (more)

Subjects/Keywords: Recommender systems (Information filtering); UCTD; Bayesian field theory; Approximate identities (Algebra); Inference

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

APA (6^{th} Edition):

Verrezen, D. (2018). Recommender systems with Bayesian aspect models and the effect of approximate inference. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/103789

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

Verrezen, Dylan. “Recommender systems with Bayesian aspect models and the effect of approximate inference.” 2018. Masters Thesis, Stellenbosch University. Accessed December 07, 2019. http://hdl.handle.net/10019.1/103789.

MLA Handbook (7^{th} Edition):

Verrezen, Dylan. “Recommender systems with Bayesian aspect models and the effect of approximate inference.” 2018. Web. 07 Dec 2019.

Vancouver:

Verrezen D. Recommender systems with Bayesian aspect models and the effect of approximate inference. [Internet] [Masters thesis]. Stellenbosch University; 2018. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10019.1/103789.

Council of Science Editors:

Verrezen D. Recommender systems with Bayesian aspect models and the effect of approximate inference. [Masters Thesis]. Stellenbosch University; 2018. Available from: http://hdl.handle.net/10019.1/103789

University of Cambridge

16.
Bui, Thang Duc.
Efficient Deterministic *Approximate* Bayesian *Inference* for Gaussian Process models
.

Degree: 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/273833

► Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian…
(more)

Subjects/Keywords: machine learning; Gaussian process; approximate inference; Bayesian statistics; supervised learning; unsupervised learning

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

APA (6^{th} Edition):

Bui, T. D. (2018). Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models . (Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273833

Not specified: Masters Thesis or Doctoral Dissertation

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

Bui, Thang Duc. “Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models .” 2018. Thesis, University of Cambridge. Accessed December 07, 2019. https://www.repository.cam.ac.uk/handle/1810/273833.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bui, Thang Duc. “Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models .” 2018. Web. 07 Dec 2019.

Vancouver:

Bui TD. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models . [Internet] [Thesis]. University of Cambridge; 2018. [cited 2019 Dec 07]. Available from: https://www.repository.cam.ac.uk/handle/1810/273833.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bui TD. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models . [Thesis]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/273833

Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

17.
Wu Navarro, Alexandre Khae.
Probabilistic machine learning for circular statistics : models and *inference* using the multivariate Generalised von Mises distribution.

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478

► Probabilistic machine learning and circular statistics—the branch of statistics concerned with data as angles and directions—are two research communities that have grown mostly in isolation…
(more)

Subjects/Keywords: Machine Learning; Circular Statistics; von Mises distribution; Gaussian Processes; Probabilistic models; Approximate Inference

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

APA (6^{th} Edition):

Wu Navarro, A. K. (2018). Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478

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

Wu Navarro, Alexandre Khae. “Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution.” 2018. Doctoral Dissertation, University of Cambridge. Accessed December 07, 2019. https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478.

MLA Handbook (7^{th} Edition):

Wu Navarro, Alexandre Khae. “Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution.” 2018. Web. 07 Dec 2019.

Vancouver:

Wu Navarro AK. Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Dec 07]. Available from: https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478.

Council of Science Editors:

Wu Navarro AK. Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478

University of Cambridge

18.
Li, Yingzhen.
*Approximate**inference* : new visions.

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977

► Nowadays machine learning (especially deep learning) techniques are being incorporated to many intelligent systems affecting the quality of human life. The ultimate purpose of these…
(more)

Subjects/Keywords: Bayesian statistics; Machine learning; Deep learning; Monte Carlo; Approximate inference; Neural networks; unsupervised learning

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

APA (6^{th} Edition):

Li, Y. (2018). Approximate inference : new visions. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977

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

Li, Yingzhen. “Approximate inference : new visions.” 2018. Doctoral Dissertation, University of Cambridge. Accessed December 07, 2019. https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977.

MLA Handbook (7^{th} Edition):

Li, Yingzhen. “Approximate inference : new visions.” 2018. Web. 07 Dec 2019.

Vancouver:

Li Y. Approximate inference : new visions. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Dec 07]. Available from: https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977.

Council of Science Editors:

Li Y. Approximate inference : new visions. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977

University of Illinois – Urbana-Champaign

19. Bean, Andrew J. Message passing algorithms - methods and applications.

Degree: PhD, Electrical & Computer Engr, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/78397

► Algorithms on graphs are used extensively in many applications and research areas. Such applications include machine learning, artificial intelligence, communications, image processing, state tracking, sensor…
(more)

Subjects/Keywords: probabilistic graphical models; approximate inference; universal portfolios; message passing; analog to digital converters (ADC)

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

APA (6^{th} Edition):

Bean, A. J. (2015). Message passing algorithms - methods and applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78397

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

Bean, Andrew J. “Message passing algorithms - methods and applications.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 07, 2019. http://hdl.handle.net/2142/78397.

MLA Handbook (7^{th} Edition):

Bean, Andrew J. “Message passing algorithms - methods and applications.” 2015. Web. 07 Dec 2019.

Vancouver:

Bean AJ. Message passing algorithms - methods and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/2142/78397.

Council of Science Editors:

Bean AJ. Message passing algorithms - methods and applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78397

EPFL

20.
Kamilov, Ulugbek.
Sparsity-Driven Statistical *Inference* for Inverse Problems.

Degree: 2015, EPFL

URL: http://infoscience.epfl.ch/record/206291

► This thesis addresses statistical *inference* for the resolution of inverse problems. Our work is motivated by the recent trend whereby classical linear methods are being…
(more)

Subjects/Keywords: Approximate message passing; belief propagation; compressive sensing; cycle spinning; inverse problems; iterative shrinkage; phase microscopy; sparsity; statistical inference; tomographic microscopy

Record Details Similar Records

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

APA (6^{th} Edition):

Kamilov, U. (2015). Sparsity-Driven Statistical Inference for Inverse Problems. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/206291

Not specified: Masters Thesis or Doctoral Dissertation

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

Kamilov, Ulugbek. “Sparsity-Driven Statistical Inference for Inverse Problems.” 2015. Thesis, EPFL. Accessed December 07, 2019. http://infoscience.epfl.ch/record/206291.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Kamilov, Ulugbek. “Sparsity-Driven Statistical Inference for Inverse Problems.” 2015. Web. 07 Dec 2019.

Vancouver:

Kamilov U. Sparsity-Driven Statistical Inference for Inverse Problems. [Internet] [Thesis]. EPFL; 2015. [cited 2019 Dec 07]. Available from: http://infoscience.epfl.ch/record/206291.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kamilov U. Sparsity-Driven Statistical Inference for Inverse Problems. [Thesis]. EPFL; 2015. Available from: http://infoscience.epfl.ch/record/206291

Not specified: Masters Thesis or Doctoral Dissertation

21.
Ko, Young Jun.
Applications of *Approximate* Learning and *Inference* for Probabilistic Models.

Degree: 2017, EPFL

URL: http://infoscience.epfl.ch/record/227482

► We develop *approximate* *inference* and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider…
(more)

Subjects/Keywords: Probabilistic models; generalized linear and bilinear models; recurrent neural networks; approximate inference; image reconstruction; implicit feedback

Record Details Similar Records

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

APA (6^{th} Edition):

Ko, Y. J. (2017). Applications of Approximate Learning and Inference for Probabilistic Models. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/227482

Not specified: Masters Thesis or Doctoral Dissertation

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

Ko, Young Jun. “Applications of Approximate Learning and Inference for Probabilistic Models.” 2017. Thesis, EPFL. Accessed December 07, 2019. http://infoscience.epfl.ch/record/227482.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ko, Young Jun. “Applications of Approximate Learning and Inference for Probabilistic Models.” 2017. Web. 07 Dec 2019.

Vancouver:

Ko YJ. Applications of Approximate Learning and Inference for Probabilistic Models. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Dec 07]. Available from: http://infoscience.epfl.ch/record/227482.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ko YJ. Applications of Approximate Learning and Inference for Probabilistic Models. [Thesis]. EPFL; 2017. Available from: http://infoscience.epfl.ch/record/227482

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Irvine

22.
Sadegh, Mojtaba.
Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian * Inference*.

Degree: Civil Engineering, 2015, University of California – Irvine

URL: http://www.escholarship.org/uc/item/067809rq

► In the past decades, Bayesian methods have found widespread application and use in environmental systems modeling. Bayes theorem states that the posterior probability of a…
(more)

Subjects/Keywords: Civil engineering; Approximate Bayesian computation; Detecting system nonstationarity; Diagnostic model analysis; Epistemic error detection; Likelihood-free inference; Process-based model analysis

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

APA (6^{th} Edition):

Sadegh, M. (2015). Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian Inference. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/067809rq

Not specified: Masters Thesis or Doctoral Dissertation

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

Sadegh, Mojtaba. “Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian Inference.” 2015. Thesis, University of California – Irvine. Accessed December 07, 2019. http://www.escholarship.org/uc/item/067809rq.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Sadegh, Mojtaba. “Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian Inference.” 2015. Web. 07 Dec 2019.

Vancouver:

Sadegh M. Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian Inference. [Internet] [Thesis]. University of California – Irvine; 2015. [cited 2019 Dec 07]. Available from: http://www.escholarship.org/uc/item/067809rq.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sadegh M. Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian Inference. [Thesis]. University of California – Irvine; 2015. Available from: http://www.escholarship.org/uc/item/067809rq

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Riverside

23.
Celikkaya, Emine Busra.
Multivariate Continuous-Time Models: *Approximate* *Inference* Algorithms and Medical Informatics Applications.

Degree: Computer Science, 2016, University of California – Riverside

URL: http://www.escholarship.org/uc/item/6179x810

► Temporal modeling of real-life systems, such as social networks, financial markets and medical decision-support systems, is important to understand them better, and make predictions. Temporal…
(more)

Subjects/Keywords: Computer science; Approximate inference algorithms; Continuous-time Bayesian networks; Continuous-time Markov processes; Continuous-time models; Gaussian processes; Medical applications

Record Details Similar Records

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

APA (6^{th} Edition):

Celikkaya, E. B. (2016). Multivariate Continuous-Time Models: Approximate Inference Algorithms and Medical Informatics Applications. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/6179x810

Not specified: Masters Thesis or Doctoral Dissertation

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

Celikkaya, Emine Busra. “Multivariate Continuous-Time Models: Approximate Inference Algorithms and Medical Informatics Applications.” 2016. Thesis, University of California – Riverside. Accessed December 07, 2019. http://www.escholarship.org/uc/item/6179x810.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Celikkaya, Emine Busra. “Multivariate Continuous-Time Models: Approximate Inference Algorithms and Medical Informatics Applications.” 2016. Web. 07 Dec 2019.

Vancouver:

Celikkaya EB. Multivariate Continuous-Time Models: Approximate Inference Algorithms and Medical Informatics Applications. [Internet] [Thesis]. University of California – Riverside; 2016. [cited 2019 Dec 07]. Available from: http://www.escholarship.org/uc/item/6179x810.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Celikkaya EB. Multivariate Continuous-Time Models: Approximate Inference Algorithms and Medical Informatics Applications. [Thesis]. University of California – Riverside; 2016. Available from: http://www.escholarship.org/uc/item/6179x810

Not specified: Masters Thesis or Doctoral Dissertation

University of Canterbury

24.
Harlow, Jennifer.
Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive * Inference*.

Degree: School of Mathematics and Statistics, 2013, University of Canterbury

URL: http://hdl.handle.net/10092/9160

► A regular paving (RP) is a finite succession of bisections that partitions a multidimensional box into sub-boxes using a binary tree-based data structure, with the…
(more)

Subjects/Keywords: statistical regular paving; real-mapped regular paving; data-adaptive histogram; multivariate histogram; nonparametric density estimation; Markov Chain Monte Carlo; regular paving approximate Bayesian computation; simulation-intensive inference

Record Details Similar Records

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

APA (6^{th} Edition):

Harlow, J. (2013). Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference. (Thesis). University of Canterbury. Retrieved from http://hdl.handle.net/10092/9160

Not specified: Masters Thesis or Doctoral Dissertation

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

Harlow, Jennifer. “Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference.” 2013. Thesis, University of Canterbury. Accessed December 07, 2019. http://hdl.handle.net/10092/9160.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Harlow, Jennifer. “Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference.” 2013. Web. 07 Dec 2019.

Vancouver:

Harlow J. Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference. [Internet] [Thesis]. University of Canterbury; 2013. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10092/9160.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Harlow J. Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference. [Thesis]. University of Canterbury; 2013. Available from: http://hdl.handle.net/10092/9160

Not specified: Masters Thesis or Doctoral Dissertation

Uppsala University

25. Eriksson, Robin. Bayesian Parameterization in the spread of Diseases.

Degree: Division of Scientific Computing, 2017, Uppsala University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326607

► Mathematical and computational epidemiological models are important tools in efforts to combat the spread of infectious diseases. The models can be used to predict…
(more)

Subjects/Keywords: Bayesian Inference; likelihood-free; Markov chain Monte Carlo; Approximate Bayesian Computations; Synthetic likelihood; Epidemiology; disease modeling; Other Computer and Information Science; Annan data- och informationsvetenskap

Record Details Similar Records

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

APA (6^{th} Edition):

Eriksson, R. (2017). Bayesian Parameterization in the spread of Diseases. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326607

Not specified: Masters Thesis or Doctoral Dissertation

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

Eriksson, Robin. “Bayesian Parameterization in the spread of Diseases.” 2017. Thesis, Uppsala University. Accessed December 07, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326607.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Eriksson, Robin. “Bayesian Parameterization in the spread of Diseases.” 2017. Web. 07 Dec 2019.

Vancouver:

Eriksson R. Bayesian Parameterization in the spread of Diseases. [Internet] [Thesis]. Uppsala University; 2017. [cited 2019 Dec 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326607.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Eriksson R. Bayesian Parameterization in the spread of Diseases. [Thesis]. Uppsala University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326607

Not specified: Masters Thesis or Doctoral Dissertation

26.
PHAM THI KIM CUC.
EMPIRICAL LIKELIHOOD, CLASSIFICATION AND *APPROXIMATE* BAYESIAN COMPUTATION.

Degree: 2016, National University of Singapore

URL: http://scholarbank.nus.edu.sg/handle/10635/132154

Subjects/Keywords: Empirical likelihood; Classification; Approximate Bayesian computation; Regression adjustment; Synthetic likelihood; Bayesian inference

Record Details Similar Records

❌

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

APA (6^{th} Edition):

CUC, P. T. K. (2016). EMPIRICAL LIKELIHOOD, CLASSIFICATION AND APPROXIMATE BAYESIAN COMPUTATION. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/132154

Not specified: Masters Thesis or Doctoral Dissertation

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

CUC, PHAM THI KIM. “EMPIRICAL LIKELIHOOD, CLASSIFICATION AND APPROXIMATE BAYESIAN COMPUTATION.” 2016. Thesis, National University of Singapore. Accessed December 07, 2019. http://scholarbank.nus.edu.sg/handle/10635/132154.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

CUC, PHAM THI KIM. “EMPIRICAL LIKELIHOOD, CLASSIFICATION AND APPROXIMATE BAYESIAN COMPUTATION.” 2016. Web. 07 Dec 2019.

Vancouver:

CUC PTK. EMPIRICAL LIKELIHOOD, CLASSIFICATION AND APPROXIMATE BAYESIAN COMPUTATION. [Internet] [Thesis]. National University of Singapore; 2016. [cited 2019 Dec 07]. Available from: http://scholarbank.nus.edu.sg/handle/10635/132154.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

CUC PTK. EMPIRICAL LIKELIHOOD, CLASSIFICATION AND APPROXIMATE BAYESIAN COMPUTATION. [Thesis]. National University of Singapore; 2016. Available from: http://scholarbank.nus.edu.sg/handle/10635/132154

Not specified: Masters Thesis or Doctoral Dissertation

27. Dehaene, Guillaume. Le statisticien neuronal : comment la perspective bayésienne peut enrichir les neurosciences : The neuronal statistician : how the Bayesian perspective can enrich neuroscience.

Degree: Docteur es, Psychologie, 2016, Sorbonne Paris Cité; Université de Genève

URL: http://www.theses.fr/2016USPCB189

►

L'inférence bayésienne répond aux questions clés de la perception, comme par exemple : "Que faut-il que je crois étant donné ce que j'ai perçu ?".… (more)

Subjects/Keywords: Neurosciences computationelles; Statistiques bayésiennes; Codage efficace; Information de Fisher; Inférence approximée; Expectation propagation; Computational neuroscience; Bayesian statistics; Efficient coding; Fisher information; Approximate inference; Expectation propagation; Large-data limit; 519.542

Record Details Similar Records

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

APA (6^{th} Edition):

Dehaene, G. (2016). Le statisticien neuronal : comment la perspective bayésienne peut enrichir les neurosciences : The neuronal statistician : how the Bayesian perspective can enrich neuroscience. (Doctoral Dissertation). Sorbonne Paris Cité; Université de Genève. Retrieved from http://www.theses.fr/2016USPCB189

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

Dehaene, Guillaume. “Le statisticien neuronal : comment la perspective bayésienne peut enrichir les neurosciences : The neuronal statistician : how the Bayesian perspective can enrich neuroscience.” 2016. Doctoral Dissertation, Sorbonne Paris Cité; Université de Genève. Accessed December 07, 2019. http://www.theses.fr/2016USPCB189.

MLA Handbook (7^{th} Edition):

Dehaene, Guillaume. “Le statisticien neuronal : comment la perspective bayésienne peut enrichir les neurosciences : The neuronal statistician : how the Bayesian perspective can enrich neuroscience.” 2016. Web. 07 Dec 2019.

Vancouver:

Dehaene G. Le statisticien neuronal : comment la perspective bayésienne peut enrichir les neurosciences : The neuronal statistician : how the Bayesian perspective can enrich neuroscience. [Internet] [Doctoral dissertation]. Sorbonne Paris Cité; Université de Genève; 2016. [cited 2019 Dec 07]. Available from: http://www.theses.fr/2016USPCB189.

Council of Science Editors:

Dehaene G. Le statisticien neuronal : comment la perspective bayésienne peut enrichir les neurosciences : The neuronal statistician : how the Bayesian perspective can enrich neuroscience. [Doctoral Dissertation]. Sorbonne Paris Cité; Université de Genève; 2016. Available from: http://www.theses.fr/2016USPCB189

University of New South Wales

28.
Rodrigues, Guilherme.
New methods for infinite and high-dimensional *approximate* Bayesian computation.

Degree: Mathematics & Statistics, 2017, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true

► The remarkable complexity of modern applied problems often requires the use of probabilistic models where the likelihood is intractable – in the sense that it…
(more)

Subjects/Keywords: Gibbs sampler; approximate Bayesian computation (ABC); Gaussian process prior; hierarchical models; indirect inferenceintractable state space models; likelihood-free inference; nonparametric density estimation; regression-adjustment

Record Details Similar Records

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

APA (6^{th} Edition):

Rodrigues, G. (2017). New methods for infinite and high-dimensional approximate Bayesian computation. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true

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

Rodrigues, Guilherme. “New methods for infinite and high-dimensional approximate Bayesian computation.” 2017. Doctoral Dissertation, University of New South Wales. Accessed December 07, 2019. http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true.

MLA Handbook (7^{th} Edition):

Rodrigues, Guilherme. “New methods for infinite and high-dimensional approximate Bayesian computation.” 2017. Web. 07 Dec 2019.

Vancouver:

Rodrigues G. New methods for infinite and high-dimensional approximate Bayesian computation. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2019 Dec 07]. Available from: http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true.

Council of Science Editors:

Rodrigues G. New methods for infinite and high-dimensional approximate Bayesian computation. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true

Kyoto University

29. Bian, Song. Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques .

Degree: 2019, Kyoto University

URL: http://hdl.handle.net/2433/242926

Subjects/Keywords: Homomorphic encryption; Approximate computing; Application-specific hardware architecture; Secure Email Filter; Secure Inference

Record Details Similar Records

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

APA (6^{th} Edition):

Bian, S. (2019). Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques . (Thesis). Kyoto University. Retrieved from http://hdl.handle.net/2433/242926

Not specified: Masters Thesis or Doctoral Dissertation

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

Bian, Song. “Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques .” 2019. Thesis, Kyoto University. Accessed December 07, 2019. http://hdl.handle.net/2433/242926.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bian, Song. “Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques .” 2019. Web. 07 Dec 2019.

Vancouver:

Bian S. Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques . [Internet] [Thesis]. Kyoto University; 2019. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/2433/242926.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bian S. Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques . [Thesis]. Kyoto University; 2019. Available from: http://hdl.handle.net/2433/242926

Not specified: Masters Thesis or Doctoral Dissertation

University of Oxford

30. McInerney, Robert E. Decision making under uncertainty.

Degree: PhD, 2014, University of Oxford

URL: http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860

► Operating and interacting in an environment requires the ability to manage uncertainty and to choose definite courses of action. In this thesis we look to…
(more)

Subjects/Keywords: 006.3; Probability theory and stochastic processes; Artificial Intelligence; Probability; Stochastic processes; Computing; Applications and algorithms; Information engineering; Robotics; Engineering & allied sciences; machine learning; probability theory; Bayesian; decision making; Reinforcement Learning; Gaussian Process; inference; approximate inference; Multi-armed Bandit; optimal decision making; uncertainty; managing uncertainty

Record Details Similar Records

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

APA (6^{th} Edition):

McInerney, R. E. (2014). Decision making under uncertainty. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860

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

McInerney, Robert E. “Decision making under uncertainty.” 2014. Doctoral Dissertation, University of Oxford. Accessed December 07, 2019. http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860.

MLA Handbook (7^{th} Edition):

McInerney, Robert E. “Decision making under uncertainty.” 2014. Web. 07 Dec 2019.

Vancouver:

McInerney RE. Decision making under uncertainty. [Internet] [Doctoral dissertation]. University of Oxford; 2014. [cited 2019 Dec 07]. Available from: http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860.

Council of Science Editors:

McInerney RE. Decision making under uncertainty. [Doctoral Dissertation]. University of Oxford; 2014. Available from: http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860