Modern methodology and applications in spatial-temporal modeling [electronic resource] / Gareth William Peters, Tomoko Matsui, editors.
2015
QA278.2
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Modern methodology and applications in spatial-temporal modeling [electronic resource] / Gareth William Peters, Tomoko Matsui, editors.
ISBN
9784431553397 electronic book
4431553398 electronic book
9784431553380
443155338X
4431553398 electronic book
9784431553380
443155338X
Published
Tokyo : Springer, 2015.
Language
English
Description
1 online resource (xv, 111 pages) : illustrations.
Call Number
QA278.2
Dewey Decimal Classification
519.5
Summary
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.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 14, 2016).
Series
SpringerBriefs in statistics. JSS research series in statistics.
Available in Other Form
Print version: 443155338X
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
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).
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).