© 2015

Modern Methodology and Applications in Spatial-Temporal Modeling

  • Gareth William Peters
  • Tomoko Matsui


  • 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 contemporary topics of importance to a diverse number of fields in spatial and temporal domains

  • Includes efficient computational statistical methods to perform analysis and inference in large spatial temporal application domains


Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Also part of the JSS Research Series in Statistics book sub series (JSSRES)

Table of contents

About this book


​ 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.


Audio and Music Signal Processing Gaussian Processes Kernel Methods Non-Parametric Bayesian Inference Wireless Signal Processing

Editors and affiliations

  • Gareth William Peters
    • 1
  • Tomoko Matsui
    • 2
  1. 1.Department of Statistical ScienceUniversity College LondonLondonUnited Kingdom
  2. 2.The Institute of Statistical MathemTachikawaJapan

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