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You searched for subject:(Approximate inference). Showing records 1 – 30 of 40 total matches.

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ETH Zürich

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

Degree: 2019, ETH Zürich

 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 (6th 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 (16th Edition):

Gotovos, Alkis. “Sampling from Probabilistic Submodular Models.” 2019. Doctoral Dissertation, ETH Zürich. Accessed December 16, 2019. http://hdl.handle.net/20.500.11850/333042.

MLA Handbook (7th Edition):

Gotovos, Alkis. “Sampling from Probabilistic Submodular Models.” 2019. Web. 16 Dec 2019.

Vancouver:

Gotovos A. Sampling from Probabilistic Submodular Models. [Internet] [Doctoral dissertation]. ETH Zürich; 2019. [cited 2019 Dec 16]. 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

 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 (6th 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 (16th Edition):

Hennig, Philipp. “Approximate inference in graphical models.” 2011. Doctoral Dissertation, University of Cambridge. Accessed December 16, 2019. https://www.repository.cam.ac.uk/handle/1810/237251 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854.

MLA Handbook (7th Edition):

Hennig, Philipp. “Approximate inference in graphical models.” 2011. Web. 16 Dec 2019.

Vancouver:

Hennig P. Approximate inference in graphical models. [Internet] [Doctoral dissertation]. University of Cambridge; 2011. [cited 2019 Dec 16]. 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

 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

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APA (6th 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 (16th Edition):

Hennig, Philipp. “Approximate inference in graphical models .” 2011. Thesis, University of Cambridge. Accessed December 16, 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 (7th Edition):

Hennig, Philipp. “Approximate inference in graphical models .” 2011. Web. 16 Dec 2019.

Vancouver:

Hennig P. Approximate inference in graphical models . [Internet] [Thesis]. University of Cambridge; 2011. [cited 2019 Dec 16]. 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

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

 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 (6th Edition):

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

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):

Lou, Qi. “Anytime Approximate Inference in Graphical Models.” 2018. Thesis, University of California – Irvine. Accessed December 16, 2019. http://www.escholarship.org/uc/item/7sc0m97f.

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

MLA Handbook (7th Edition):

Lou, Qi. “Anytime Approximate Inference in Graphical Models.” 2018. Web. 16 Dec 2019.

Vancouver:

Lou Q. Anytime Approximate Inference in Graphical Models. [Internet] [Thesis]. University of California – Irvine; 2018. [cited 2019 Dec 16]. Available from: http://www.escholarship.org/uc/item/7sc0m97f.

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

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

  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

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APA (6th 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 (16th Edition):

Neville, Sarah Elizabeth. “Elaborate distribution semiparametric regression via mean field variational Bayes.” 2013. Doctoral Dissertation, University of Wollongong. Accessed December 16, 2019. 0103 NUMERICAL AND COMPUTATIONAL MATHEMATICS, 0104 STATISTICS ; https://ro.uow.edu.au/theses/3958.

MLA Handbook (7th Edition):

Neville, Sarah Elizabeth. “Elaborate distribution semiparametric regression via mean field variational Bayes.” 2013. Web. 16 Dec 2019.

Vancouver:

Neville SE. Elaborate distribution semiparametric regression via mean field variational Bayes. [Internet] [Doctoral dissertation]. University of Wollongong; 2013. [cited 2019 Dec 16]. 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

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

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APA (6th 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 (16th Edition):

Dhoot, Aditya. “Wind Farm Layout Optimization Using Approximate Inference in Graphical Models.” 2016. Masters Thesis, University of Toronto. Accessed December 16, 2019. http://hdl.handle.net/1807/72692.

MLA Handbook (7th Edition):

Dhoot, Aditya. “Wind Farm Layout Optimization Using Approximate Inference in Graphical Models.” 2016. Web. 16 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 16]. 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

 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

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APA (6th 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 (16th Edition):

Alaghband, Mohammad-Reza. “Inference for stochastic models of molecular data.” 2009. Doctoral Dissertation, University of Southern California. Accessed December 16, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/517465/rec/3470.

MLA Handbook (7th Edition):

Alaghband, Mohammad-Reza. “Inference for stochastic models of molecular data.” 2009. Web. 16 Dec 2019.

Vancouver:

Alaghband M. Inference for stochastic models of molecular data. [Internet] [Doctoral dissertation]. University of Southern California; 2009. [cited 2019 Dec 16]. 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

 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 (6th 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 (16th Edition):

Wang, Yi. “Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness.” 2016. Doctoral Dissertation, University of New South Wales. Accessed December 16, 2019. http://handle.unsw.edu.au/1959.4/56818 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41495/SOURCE02?view=true.

MLA Handbook (7th Edition):

Wang, Yi. “Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness.” 2016. Web. 16 Dec 2019.

Vancouver:

Wang Y. Bayesian nonparametric probabilistic clustering: robustness and parsimoniousness. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Dec 16]. 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

  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 (6th Edition):

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

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):

Heard, Daniel Philip. “Statistical Inference Utilizing Agent Based Models .” 2014. Thesis, Duke University. Accessed December 16, 2019. http://hdl.handle.net/10161/8687.

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

MLA Handbook (7th Edition):

Heard, Daniel Philip. “Statistical Inference Utilizing Agent Based Models .” 2014. Web. 16 Dec 2019.

Vancouver:

Heard DP. Statistical Inference Utilizing Agent Based Models . [Internet] [Thesis]. Duke University; 2014. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/10161/8687.

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

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


Duke University

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

Degree: 2018, Duke University

  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

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APA (6th Edition):

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

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):

Tan, Zilong. “Approximate Inference for High-Dimensional Latent Variable Models .” 2018. Thesis, Duke University. Accessed December 16, 2019. http://hdl.handle.net/10161/18280.

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

MLA Handbook (7th Edition):

Tan, Zilong. “Approximate Inference for High-Dimensional Latent Variable Models .” 2018. Web. 16 Dec 2019.

Vancouver:

Tan Z. Approximate Inference for High-Dimensional Latent Variable Models . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/10161/18280.

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

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

 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 (6th 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

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):

Liu, Qiang. “Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework.” 2014. Thesis, University of California – Irvine. Accessed December 16, 2019. http://www.escholarship.org/uc/item/92p8w3xb.

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

MLA Handbook (7th Edition):

Liu, Qiang. “Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework.” 2014. Web. 16 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 16]. Available from: http://www.escholarship.org/uc/item/92p8w3xb.

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

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

 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

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APA (6th 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 (16th 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 16, 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 (7th Edition):

Daly, Aidan C. “Statistical tools and community resources for developing trusted models in biology and chemistry.” 2017. Web. 16 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 16]. 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

 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)

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APA (6th Edition):

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

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):

Bruce-Doust, Riley. “Forgetting properties of finite-state reciprocal processes.” 2017. Thesis, University of Adelaide. Accessed December 16, 2019. http://hdl.handle.net/2440/113380.

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

MLA Handbook (7th Edition):

Bruce-Doust, Riley. “Forgetting properties of finite-state reciprocal processes.” 2017. Web. 16 Dec 2019.

Vancouver:

Bruce-Doust R. Forgetting properties of finite-state reciprocal processes. [Internet] [Thesis]. University of Adelaide; 2017. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/2440/113380.

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

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

 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 (6th 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 (16th Edition):

Bui, Thang Duc. “Efficient deterministic approximate Bayesian inference for Gaussian process models.” 2018. Doctoral Dissertation, University of Cambridge. Accessed December 16, 2019. https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600.

MLA Handbook (7th Edition):

Bui, Thang Duc. “Efficient deterministic approximate Bayesian inference for Gaussian process models.” 2018. Web. 16 Dec 2019.

Vancouver:

Bui TD. Efficient deterministic approximate Bayesian inference for Gaussian process models. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Dec 16]. 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

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 (6th 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 (16th Edition):

Verrezen, Dylan. “Recommender systems with Bayesian aspect models and the effect of approximate inference.” 2018. Masters Thesis, Stellenbosch University. Accessed December 16, 2019. http://hdl.handle.net/10019.1/103789.

MLA Handbook (7th Edition):

Verrezen, Dylan. “Recommender systems with Bayesian aspect models and the effect of approximate inference.” 2018. Web. 16 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 16]. 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

 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 (6th 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

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):

Bui, Thang Duc. “Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models .” 2018. Thesis, University of Cambridge. Accessed December 16, 2019. https://www.repository.cam.ac.uk/handle/1810/273833.

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

MLA Handbook (7th Edition):

Bui, Thang Duc. “Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models .” 2018. Web. 16 Dec 2019.

Vancouver:

Bui TD. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models . [Internet] [Thesis]. University of Cambridge; 2018. [cited 2019 Dec 16]. Available from: https://www.repository.cam.ac.uk/handle/1810/273833.

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

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

 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 (6th 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 (16th 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 16, 2019. https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478.

MLA Handbook (7th Edition):

Wu Navarro, Alexandre Khae. “Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution.” 2018. Web. 16 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 16]. 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

 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 (6th 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 (16th Edition):

Li, Yingzhen. “Approximate inference : new visions.” 2018. Doctoral Dissertation, University of Cambridge. Accessed December 16, 2019. https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977.

MLA Handbook (7th Edition):

Li, Yingzhen. “Approximate inference : new visions.” 2018. Web. 16 Dec 2019.

Vancouver:

Li Y. Approximate inference : new visions. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Dec 16]. 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

 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 (6th 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 (16th Edition):

Bean, Andrew J. “Message passing algorithms - methods and applications.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 16, 2019. http://hdl.handle.net/2142/78397.

MLA Handbook (7th Edition):

Bean, Andrew J. “Message passing algorithms - methods and applications.” 2015. Web. 16 Dec 2019.

Vancouver:

Bean AJ. Message passing algorithms - methods and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Dec 16]. 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

 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

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

APA (6th Edition):

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

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):

Kamilov, Ulugbek. “Sparsity-Driven Statistical Inference for Inverse Problems.” 2015. Thesis, EPFL. Accessed December 16, 2019. http://infoscience.epfl.ch/record/206291.

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

MLA Handbook (7th Edition):

Kamilov, Ulugbek. “Sparsity-Driven Statistical Inference for Inverse Problems.” 2015. Web. 16 Dec 2019.

Vancouver:

Kamilov U. Sparsity-Driven Statistical Inference for Inverse Problems. [Internet] [Thesis]. EPFL; 2015. [cited 2019 Dec 16]. Available from: http://infoscience.epfl.ch/record/206291.

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

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

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

Degree: 2017, EPFL

 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

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

APA (6th Edition):

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

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):

Ko, Young Jun. “Applications of Approximate Learning and Inference for Probabilistic Models.” 2017. Thesis, EPFL. Accessed December 16, 2019. http://infoscience.epfl.ch/record/227482.

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

MLA Handbook (7th Edition):

Ko, Young Jun. “Applications of Approximate Learning and Inference for Probabilistic Models.” 2017. Web. 16 Dec 2019.

Vancouver:

Ko YJ. Applications of Approximate Learning and Inference for Probabilistic Models. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Dec 16]. Available from: http://infoscience.epfl.ch/record/227482.

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

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

 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 (6th 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

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):

Sadegh, Mojtaba. “Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian Inference.” 2015. Thesis, University of California – Irvine. Accessed December 16, 2019. http://www.escholarship.org/uc/item/067809rq.

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

MLA Handbook (7th Edition):

Sadegh, Mojtaba. “Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian Inference.” 2015. Web. 16 Dec 2019.

Vancouver:

Sadegh M. Towards Improved Model Evaluation: Diagnostic, Likelihood-free, Bayesian Inference. [Internet] [Thesis]. University of California – Irvine; 2015. [cited 2019 Dec 16]. Available from: http://www.escholarship.org/uc/item/067809rq.

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

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

 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

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

APA (6th 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

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):

Celikkaya, Emine Busra. “Multivariate Continuous-Time Models: Approximate Inference Algorithms and Medical Informatics Applications.” 2016. Thesis, University of California – Riverside. Accessed December 16, 2019. http://www.escholarship.org/uc/item/6179x810.

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

MLA Handbook (7th Edition):

Celikkaya, Emine Busra. “Multivariate Continuous-Time Models: Approximate Inference Algorithms and Medical Informatics Applications.” 2016. Web. 16 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 16]. Available from: http://www.escholarship.org/uc/item/6179x810.

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

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

 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

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

APA (6th 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

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):

Harlow, Jennifer. “Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference.” 2013. Thesis, University of Canterbury. Accessed December 16, 2019. http://hdl.handle.net/10092/9160.

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

MLA Handbook (7th Edition):

Harlow, Jennifer. “Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference.” 2013. Web. 16 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 16]. Available from: http://hdl.handle.net/10092/9160.

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

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

  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

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

APA (6th 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

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):

Eriksson, Robin. “Bayesian Parameterization in the spread of Diseases.” 2017. Thesis, Uppsala University. Accessed December 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326607.

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

MLA Handbook (7th Edition):

Eriksson, Robin. “Bayesian Parameterization in the spread of Diseases.” 2017. Web. 16 Dec 2019.

Vancouver:

Eriksson R. Bayesian Parameterization in the spread of Diseases. [Internet] [Thesis]. Uppsala University; 2017. [cited 2019 Dec 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326607.

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

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

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

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

APA (6th 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

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):

CUC, PHAM THI KIM. “EMPIRICAL LIKELIHOOD, CLASSIFICATION AND APPROXIMATE BAYESIAN COMPUTATION.” 2016. Thesis, National University of Singapore. Accessed December 16, 2019. http://scholarbank.nus.edu.sg/handle/10635/132154.

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

MLA Handbook (7th Edition):

CUC, PHAM THI KIM. “EMPIRICAL LIKELIHOOD, CLASSIFICATION AND APPROXIMATE BAYESIAN COMPUTATION.” 2016. Web. 16 Dec 2019.

Vancouver:

CUC PTK. EMPIRICAL LIKELIHOOD, CLASSIFICATION AND APPROXIMATE BAYESIAN COMPUTATION. [Internet] [Thesis]. National University of Singapore; 2016. [cited 2019 Dec 16]. Available from: http://scholarbank.nus.edu.sg/handle/10635/132154.

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

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

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

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APA (6th 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 (16th 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 16, 2019. http://www.theses.fr/2016USPCB189.

MLA Handbook (7th 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. 16 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 16]. 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

 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

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

APA (6th 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 (16th Edition):

Rodrigues, Guilherme. “New methods for infinite and high-dimensional approximate Bayesian computation.” 2017. Doctoral Dissertation, University of New South Wales. Accessed December 16, 2019. http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true.

MLA Handbook (7th Edition):

Rodrigues, Guilherme. “New methods for infinite and high-dimensional approximate Bayesian computation.” 2017. Web. 16 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 16]. 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

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

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

APA (6th Edition):

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

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):

Bian, Song. “Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques .” 2019. Thesis, Kyoto University. Accessed December 16, 2019. http://hdl.handle.net/2433/242926.

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

MLA Handbook (7th Edition):

Bian, Song. “Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques .” 2019. Web. 16 Dec 2019.

Vancouver:

Bian S. Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques . [Internet] [Thesis]. Kyoto University; 2019. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/2433/242926.

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

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


University of Oxford

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

Degree: PhD, 2014, University of Oxford

 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

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

APA (6th 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 (16th Edition):

McInerney, Robert E. “Decision making under uncertainty.” 2014. Doctoral Dissertation, University of Oxford. Accessed December 16, 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 (7th Edition):

McInerney, Robert E. “Decision making under uncertainty.” 2014. Web. 16 Dec 2019.

Vancouver:

McInerney RE. Decision making under uncertainty. [Internet] [Doctoral dissertation]. University of Oxford; 2014. [cited 2019 Dec 16]. 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

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