LEADER 04253nam 22006735 450 001 9910300253303321 005 20250408140815.0 010 $a4-431-55339-8 024 7 $a10.1007/978-4-431-55339-7 035 $a(CKB)3710000000571766 035 $a(EBL)4332308 035 $a(SSID)ssj0001607022 035 $a(PQKBManifestationID)16317735 035 $a(PQKBTitleCode)TC0001607022 035 $a(PQKBWorkID)14896924 035 $a(PQKB)10653149 035 $a(DE-He213)978-4-431-55339-7 035 $a(MiAaPQ)EBC4332308 035 $a(PPN)19170105X 035 $a(EXLCZ)993710000000571766 100 $a20160108d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModern Methodology and Applications in Spatial-Temporal Modeling /$fedited by Gareth William Peters, Tomoko Matsui 205 $a1st ed. 2015. 210 1$aTokyo :$cSpringer Japan :$cImprint: Springer,$d2015. 215 $a1 online resource (123 p.) 225 1 $aJSS Research Series in Statistics,$x2364-0065 300 $aDescription based upon print version of record. 311 08$a4-431-55338-X 320 $aIncludes bibliographical references at the end of each chapters. 327 $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). 330 $aThis book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models. 410 0$aJSS Research Series in Statistics,$x2364-0065 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a519.536 702 $aPeters$b Gareth William$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMatsui$b Tomoko$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910300253303321 996 $aModern methodology and applications in spatial-temporal modeling$91522873 997 $aUNINA