WORKSHOP: Stochastic Filtering and Control

概率论的进步，使新的计算算法成为可能的计算能力的增加，以及统计信号处理，工程和数学金融的重要现代应用，重新引起了人们对随机滤波和随机控制领域的兴趣。在过去几年中，它们在理论和实践方面都取得了重要进展。在随机滤波中，目标是从部分（可能有噪声）观测中估计随机过程。应用广泛，如导弹跟踪和手写识别。在过去的几年里，该领域取得了巨大的进步，主要是由于发现了一套新的强大的算法（顺序蒙特卡罗方法或粒子滤波器）。这些新的算法和发现的错误，在一个40岁的证明也重新关注老问题的渐近稳定性，即如何质量的估计取决于初始化的algorithm.In随机控制，目标是最优控制的过程，以尽量减少风险函数，或等价地，以最大化的效用函数。这样的问题出现在大多数工程领域，从结构工程到金融工程。最近，然而，一个巨大的刺激，在随机控制的进一步发展是发现新的应用数学金融，这是随后发现了许多新的数学工具，创造了处理新的应用。我们的目标是汇集领先的研究人员在这些领域的不同方面工作/理论，算法或应用/和从两个领域的随机滤波和随机控制，它们是密切相关的（例如，控制部分观察到的过程）。我们希望，这将使人们更全面地了解已经取得的进展和仍然面临的挑战。我们认为，不同领域之间存在着相互促进的重要机会。

Advances in probability theory, the increase in computing power that made new computational algorithms possible and important modern applications from statistical signal processing, engineering and mathematical finance have renewed interest in the fields of stochastic filtering and stochastic control. During the last few years, there has been important progress in both their theory and practice. In stochastic filtering, the goal is to estimate a random process from partial (possibly noisy) observations. Applications are widespread, such as missile tracking and handwriting recognition. In the last few years, the field has seen dramatic progress, mainly due to the discovery of a new set of powerful algorithms (sequential Monte Carlo methods or Particle Filters). These new algorithms and the discovery of an error in a forty-year-old proof also renewed interest in the old problem of asymptotic stability, i.e. how the quality of the estimation depends on the initialisation of the algorithm.In stochastic control, the goal is to optimally control a process in order to minimize a risk function or, equivalently, to maximise a utility function. Such problems come up in most fields of engineering, from structural to financial engineering. Recently, however, a huge stimulus to the further development in stochastic control was the discovery of new applications to mathematical finance, which was followed by the discovery of many new mathematical tools created to deal with the new application.We aim to bring together leading researchers working on different aspects of these fields / theory, algorithms or applications / and from both fields of stochastic filtering and stochastic control, which are closely related (for example, in control of partially observed processes). We hope that this will give a fuller picture of the progress that has been made and the challenges that still lie ahead. We believe that there is a significant opportunity for cross-fertilization between the different areas.