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Titlebook: Modern Methodology and Applications in Spatial-Temporal Modeling; Gareth William Peters,Tomoko Matsui Book 2015 The Author(s) 2015 Audio a

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發(fā)表于 2025-3-21 20:04:13 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Modern Methodology and Applications in Spatial-Temporal Modeling
編輯Gareth William Peters,Tomoko Matsui
視頻videohttp://file.papertrans.cn/638/637253/637253.mp4
概述Covers specialized topics in spatial-temporal modeling provided by world experts for an introduction to key components.Discusses a rigorous probabilistic and statistical framework for a range of conte
叢書名稱SpringerBriefs in Statistics
圖書封面Titlebook: Modern Methodology and Applications in Spatial-Temporal Modeling;  Gareth William Peters,Tomoko Matsui Book 2015 The Author(s) 2015 Audio a
描述?This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet pr
出版日期Book 2015
關(guān)鍵詞Audio and Music Signal Processing; Gaussian Processes; Kernel Methods; Non-Parametric Bayesian Inferenc
版次1
doihttps://doi.org/10.1007/978-4-431-55339-7
isbn_softcover978-4-431-55338-0
isbn_ebook978-4-431-55339-7Series ISSN 2191-544X Series E-ISSN 2191-5458
issn_series 2191-544X
copyrightThe Author(s) 2015
The information of publication is updating

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SpringerBriefs in Statisticshttp://image.papertrans.cn/m/image/637253.jpg
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https://doi.org/10.1007/978-4-431-55339-7Audio and Music Signal Processing; Gaussian Processes; Kernel Methods; Non-Parametric Bayesian Inferenc
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Book 2015ed involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant
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