| 书目名称 | Modern Methodology and Applications in Spatial-Temporal Modeling |
| 编辑 | Gareth William Peters,Tomoko Matsui |
| 视频video | |
| 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 |
| 丛书名称 | SpringerBriefs in Statistics |
| 图书封面 |  |
| 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 |
| 关键词 | Audio and Music Signal Processing; Gaussian Processes; Kernel Methods; Non-Parametric Bayesian Inferenc |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-4-431-55339-7 |
| isbn_softcover | 978-4-431-55338-0 |
| isbn_ebook | 978-4-431-55339-7Series ISSN 2191-544X Series E-ISSN 2191-5458 |
| issn_series | 2191-544X |
| copyright | The Author(s) 2015 |
发表于 2025-3-21 20:04:13
