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Modern methodology and applications ...

Matsui, Tomoko.

Modern methodology and applications in spatial-temporal modeling

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modern methodology and applications in spatial-temporal modelingedited by Gareth William Peters, Tomoko Matsui.
其他作者:	Peters, Gareth William.
出版者:	Tokyo :Springer Japan :2015.
面頁冊數:	xv, 111 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Spatial analysis (Statistics)
電子資源:	http://dx.doi.org/10.1007/978-4-431-55339-7
ISBN:	9784431553397$q(electronic bk.)

Modern methodology and applications in spatial-temporal modeling
Modern methodology and applications in spatial-temporal modeling[electronic resource] /edited by Gareth William Peters, Tomoko Matsui. - Tokyo :Springer Japan :2015. - xv, 111 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

1 Nonparametric Bayesian Inference with Kernel Mean Embedding (Kenji Fukumizu) -- 2 How to Utilise Sensor Network Data to Efficiently Perform Model Calibration and Spatial Field Reconstruction (Gareth W. Peters, Ido Nevat and Tomoko Matsui) -- 3 Speech and Music Emotion Recognition using Gaussian Processes (Konstantin Markov and Tomoko Matsui) -- 4 Topic Modeling for Speech and Language Processing (Jen-Tzung Chien)

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 processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.

ISBN: 9784431553397$q(electronic bk.)

Standard No.: 10.1007/978-4-431-55339-7doiSubjects--Topical Terms:

185012
Spatial analysis (Statistics)

LC Class. No.: QA278.2

Dewey Class. No.: 519.5

Modern methodology and applications in spatial-temporal modeling
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520 $a 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 processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.
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