STM2015 & CSM2015

13/July/2015 (Mon) - 17/July/2015 (Fri)

Admission Free

Institute of Statistical Mathematics, Tokyo, Japan
Email your intention to attend to stm2015アットマーク
Gareth W. Peters (UCL) and Tomoko Matsui (ISM)
The topics
  1. Heavy tailed processes and their characterizations: Levy Processes and Stable Processes - with applications in wireless communications, finance, insurance and speech.
  2. Counting processes and branching processes - with applications in earthquake dynamic modelling, signal processing and speech and audio processing.
  3. State space modelling and high-dimensional complex systems - with methodology and applications in signal processing, sensor networks and environmental modelling and finance and insurance.
  4. Statistical machine learning methods - with applications in kernel methods and Gaussian process state space modelling.
- Prof. N. Azzaoui
- Prof. Y. Kawasaki
- Prof. F. Phoa
- Mr. M. Ames
- Prof. M. Maejima
- Prof. F. Septier
- Dr. G. Bagnarosa
- Prof. K. Markov
- Prof. P. Shevchenko
- Prof. L. Clavier
- Prof. T. Matsui
- Prof. T. Suzuki
- Prof. J.T. Chien
- Dr. D. Murukami
- Ms. Jin Xin
- Dr. M. Egan
- Prof. K. Fukumizu
- Dr. I. Nevat
- Prof. S. Zhou
- Prof. N. Ikoma
- Prof. Y. Ogata
- Prof. J. Zhuang
- Prof. S. Kou
- Dr. G.W. Peters

The analysis of complex and massive data sets which display attributes of spatial and temporal characteristics is a growing field of research. Traditionally the two fields have been treated predominantly via a range of different approaches, depending on the discipline in which the applications are under study. For instance in spatial statistics and geo-statistics there is a long history of spatial modeling via regression models, random fields and parametric approaches. There is also a large literature on spatial extremes and the analysis of such features of spatial temporal data. In machine learning there are new approaches being considered based on semi-parametric and non-parametric modeling paradigms which incorporate Bayesian modeling. Then in the signal processing and engineering communities there have been a range of frequency and spatial methods developed based on filters, parametric modeling, regression - basis expansion models, wavelets regression models, and splines .

There has been a range of recent developments in characterizing multivariate spatial and temporal processes which are either discrete (branching and counting processes) or continuous (heavy tailed processes such as Levy processes) and their sub-families the stable processes and Gaussian processes. In addition the study of such processes in practical applications has advanced significantly and the intention of the workshops is to present some recent developments in specification, estimation in high dimensional and complex structured models formed from such processes and application.


Please note the PhD and Postdocs are encourage to attend and present their research in a poster session that will take place on 15th of July between 5:00pm-6:30pm. If you are interested to present a poster - please register your interest by emailing
with your name, affiliation, Poster Title and Abstract. You will be notified of acceptance in the poster session by 19th of June.

Previous years workshop details are available at

  1. STM2014 and CSM2014
  2. STM2013

Springer Briefs Special Issue from 2014

book1:Theoretical Aspects of Spatial-Temporal Modeling
book2:Modern Methodology and Applications in Spatial-Temporal Modeling