Particle Filters and Markov Chains for Learning of Dynamical Systems.

Particle Filters and Markov Chains for Learning of Dynamical Systems.

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Superior document:Linköping Studies in Science and Technology. Dissertations Series ; v.1530
:
Lindsten, Fredrik.
Place / Publishing House:Linköping : : Linkopings Universitet,, 2013.
{copy}2013.
Year of Publication:2013
Edition:1st ed.
Language:English
Series:Linköping Studies in Science and Technology. Dissertations Series
Online Access:
Physical Description:1 online resource (65 pages)
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(CaPaEBR)ebr10783627
(OCoLC)927227343
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record_format marc
spelling Lindsten, Fredrik.
Particle Filters and Markov Chains for Learning of Dynamical Systems.
1st ed.
Linköping : Linkopings Universitet, 2013.
{copy}2013.
1 online resource (65 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Linköping Studies in Science and Technology. Dissertations Series ; v.1530
Intro -- Abstract -- Populärvetenskaplig sammanfattning -- Acknowledgments -- Contents -- Notation -- I Background -- 1 Introduction -- 1.1 Models of dynamical systems -- 1.2 Inference and learning -- 1.3 Contributions -- 1.4 Publications -- 1.5 Thesis outline -- 1.5.1 Outline of Part I -- 1.5.2 Outline of Part II -- 2 Learning of dynamical systems -- 2.1 Modeling -- 2.2 Maximum likelihood -- 2.3 Bayesian learning -- 2.4 Data augmentation -- 2.5 Online learning -- 3 Monte Carlo methods -- 3.1 The Monte Carlo idea -- 3.2 Rejection Sampling -- 3.3 Importance sampling -- 3.4 Particle filters and Markov chains -- 3.5 Rao-Blackwellization -- 4 Concluding remarks -- 4.1 Conclusions and future work -- 4.2 Further reading -- Bibliography.
Description based on publisher supplied metadata and other sources.
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Electronic books.
Print version: Lindsten, Fredrik Particle Filters and Markov Chains for Learning of Dynamical Systems Linköping : Linkopings Universitet,c2013
ProQuest (Firm)
Linköping Studies in Science and Technology. Dissertations Series
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=3328047 Click to View
language English
format eBook
author Lindsten, Fredrik.
spellingShingle Lindsten, Fredrik.
Particle Filters and Markov Chains for Learning of Dynamical Systems.
Linköping Studies in Science and Technology. Dissertations Series ;
Intro -- Abstract -- Populärvetenskaplig sammanfattning -- Acknowledgments -- Contents -- Notation -- I Background -- 1 Introduction -- 1.1 Models of dynamical systems -- 1.2 Inference and learning -- 1.3 Contributions -- 1.4 Publications -- 1.5 Thesis outline -- 1.5.1 Outline of Part I -- 1.5.2 Outline of Part II -- 2 Learning of dynamical systems -- 2.1 Modeling -- 2.2 Maximum likelihood -- 2.3 Bayesian learning -- 2.4 Data augmentation -- 2.5 Online learning -- 3 Monte Carlo methods -- 3.1 The Monte Carlo idea -- 3.2 Rejection Sampling -- 3.3 Importance sampling -- 3.4 Particle filters and Markov chains -- 3.5 Rao-Blackwellization -- 4 Concluding remarks -- 4.1 Conclusions and future work -- 4.2 Further reading -- Bibliography.
author_facet Lindsten, Fredrik.
author_variant f l fl
author_sort Lindsten, Fredrik.
title Particle Filters and Markov Chains for Learning of Dynamical Systems.
title_full Particle Filters and Markov Chains for Learning of Dynamical Systems.
title_fullStr Particle Filters and Markov Chains for Learning of Dynamical Systems.
title_full_unstemmed Particle Filters and Markov Chains for Learning of Dynamical Systems.
title_auth Particle Filters and Markov Chains for Learning of Dynamical Systems.
title_new Particle Filters and Markov Chains for Learning of Dynamical Systems.
title_sort particle filters and markov chains for learning of dynamical systems.
series Linköping Studies in Science and Technology. Dissertations Series ;
series2 Linköping Studies in Science and Technology. Dissertations Series ;
publisher Linkopings Universitet,
publishDate 2013
physical 1 online resource (65 pages)
edition 1st ed.
contents Intro -- Abstract -- Populärvetenskaplig sammanfattning -- Acknowledgments -- Contents -- Notation -- I Background -- 1 Introduction -- 1.1 Models of dynamical systems -- 1.2 Inference and learning -- 1.3 Contributions -- 1.4 Publications -- 1.5 Thesis outline -- 1.5.1 Outline of Part I -- 1.5.2 Outline of Part II -- 2 Learning of dynamical systems -- 2.1 Modeling -- 2.2 Maximum likelihood -- 2.3 Bayesian learning -- 2.4 Data augmentation -- 2.5 Online learning -- 3 Monte Carlo methods -- 3.1 The Monte Carlo idea -- 3.2 Rejection Sampling -- 3.3 Importance sampling -- 3.4 Particle filters and Markov chains -- 3.5 Rao-Blackwellization -- 4 Concluding remarks -- 4.1 Conclusions and future work -- 4.2 Further reading -- Bibliography.
isbn 9789175195599
genre Electronic books.
genre_facet Electronic books.
url https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=3328047
illustrated Not Illustrated
oclc_num 927227343
work_keys_str_mv AT lindstenfredrik particlefiltersandmarkovchainsforlearningofdynamicalsystems
status_str n
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hierarchy_parent_title Linköping Studies in Science and Technology. Dissertations Series ; v.1530
is_hierarchy_title Particle Filters and Markov Chains for Learning of Dynamical Systems.
container_title Linköping Studies in Science and Technology. Dissertations Series ; v.1530
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