Workshop on Approximate Inference in Stochastic Processes and Dynamical Systems, Cumberland Lodge 2008
The modelling of continuous-time stochastic processes from uncertain (discrete) observations is an important task that arises in a wide range of applications, such as in climate modelling, tracking, finance and systems biology. Although observations are in general only available at discrete times, the underlying system is often a continuous-time one. Hence, the physics or the dynamics are formulated by systems of differential equations, the observation noise and the process uncertainty being modelled by several stochastic sources. When dealing with stochastic processes, it is natural to take a probabilistic approach. For example, we may incorporate prior knowledge about the dynamics by providing probability distributions on the unknown functions. In contrast to models that are only data driven, it is hoped that incorporating domain knowledge in the inference process will improve performance in practice. The main challenges in this context are how to deal with continuous-time objects, how to do inference and how to be agnostic about the deterministic driving forces and the sources of uncertainty.
The workshop provides a forum for discussing the open problems arising in dynamical systems, and in particular continuous-time stochastic processes. It focuses both on the mathematical aspects/theoretical advances and the applications. Another important aim is to bridge the gap between the different communities (data assimilation, machine learning, optimal control, systems biology, finance, ...) and favour interactions. Hence, the workshop is of interest to researchers from statistics, computer science, mathematics, physics and engineering. We also hope that the workshop provides new insights in this exciting field and serve as a starting point for new research perspectives and future collaborations. The workshop is sponsored by PASCAL2 network of excellence and is one of six workshops in the Thematic Programme in Leveraging Complex Prior Knowledge for Learning.
For more inforamtion visit the Workshop website.
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