EAN: 9784431553380

SPRINGERBRIEFS IN STATISTICS MODERN METHODOLO...

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.

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