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DYNSTOCH 2012

Statistical Methods for Dynamical Stochastic Models


7-9 June 2012
Institut Henri Poincaré, Amphithéatre Perrin


The DYNSTOCH  network was former in the period 2000 - 2004 a research training network under the programme Improving Human Potential financed by the The Fifth Framework Programme of the European Commission. The research activities continue, and the network infrastructure is continued to the extent possible without funding. Since 2004,   annual workshops are  held. It takes place in 2012 in Paris, at the Institut Henri Poincaré.

In many fields complex dynamical stochastic models are needed to describe processes that develop in time and/or space in a random way, usually with temporal or spatial interactions that are important for a proper understanding of the phenomenon under study and for making predictions about the system. A few concrete examples of such stochastic processes are:   

(1) Biomedical data from pharmakocinetics and pharmacodynamics, neuronal and epidemics data, population dynamics in genetics and ecology; (2) Financial data : statistical inference for local volatility and stochastic volatility models, Levy models, and for high frequency data; (3) Turbulent flows, functioning of telecommunication network and other complex technological systems. 

The high speed of present day computers has made use of complex stochastic models feasible, and at the same time, the important developments that have taken place in probability theory, in particular in the area of stochastic calculus, have only to a limited extent been used by statisticians to develop statistical methods for stochastic processes.

The principal aim of the DYNSTOCH network is to make a major contribution to the theory of statistical inference for stochastic processes by taking advantage of the tools of modern probability theory including stochastic calculus, stochastic algorithms and by using highly computer-intensive methods.

The principal objectives of the DYNSTOCH network are: (1) Development of statistical methods for continuous-time stochastic processes and the study of the properties of the resulting procedures; (2) Development of asymptotic statistical theory for stochastic processes; (3) Modelling and statistical data analysis in biology, finance, turbulence and telecommunication.