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000742284 020__ $$a9784431553397$$qelectronic book
000742284 020__ $$a4431553398$$qelectronic book
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000742284 08204 $$a519.5$$223
000742284 24500 $$aModern methodology and applications in spatial-temporal modeling$$h[electronic resource] /$$cGareth William Peters, Tomoko Matsui, editors.
000742284 264_1 $$aTokyo :$$bSpringer,$$c2015.
000742284 300__ $$a1 online resource (xv, 111 pages) :$$billustrations.
000742284 336__ $$atext$$btxt$$2rdacontent
000742284 337__ $$acomputer$$bc$$2rdamedia
000742284 338__ $$aonline resource$$bcr$$2rdacarrier
000742284 4901_ $$aSpringerBriefs in statistics, JSS research series in statistics,$$x2191-544X
000742284 5050_ $$a1 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).
000742284 506__ $$aAccess limited to authorized users.
000742284 520__ $$aThis 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.
000742284 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 14, 2016).
000742284 650_0 $$aSpatial analysis (Statistics)
000742284 7001_ $$aPeters, Gareth W.,$$d1978-$$eeditor.
000742284 7001_ $$aMatsui, Tomoko,$$eeditor.
000742284 77608 $$iPrint version:$$z9784431553380$$z443155338X$$w(OCoLC)898529447
000742284 830_0 $$aSpringerBriefs in statistics.$$pJSS research series in statistics.
000742284 85280 $$bebk$$hSpringerLink
000742284 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-4-431-55339-7$$zOnline Access$$91397441.1
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