About
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.
Videos

Approximate system identification: Misfit versus latency
Aug 5, 2008
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4318 views

Normalized kernel-weighted random measures
Aug 5, 2008
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3598 views

Estimating the probability of rare climate events: inference from a large determ...
Sep 9, 2008
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3377 views

Solving the data association problem in multi-object tracking by Fourier analysi...
Aug 8, 2008
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7467 views

Sigma point and particle approximations of stochastic differential equations in ...
Aug 5, 2008
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6470 views

On stratified path sampling of the Thermodynamic Integral: computing Bayes facto...
Aug 5, 2008
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5847 views

Exact simulation of jump diffusions
Aug 5, 2008
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4323 views

Gaussian process toolkit for modelling the dynamics of transcriptional regulatio...
Aug 5, 2008
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4157 views

An efficient approach to stochastic optimal control
Aug 5, 2008
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11863 views

An efficient Monte-Carlo algorithm for the ML-Type II parameter estimation of no...
Aug 5, 2008
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3815 views

An introduction to Levy processes with financial modelling in mind
Aug 5, 2008
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18016 views

Density estimation of initial conditions for populations of dynamical systems
Aug 5, 2008
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3824 views

Sparse Multi-output Gaussian Processes
Aug 5, 2008
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5481 views

Variational inference and learning for continuous-time nonlinear state-space mod...
Aug 5, 2008
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3363 views

Variational filtering in generated coordinates of motion
Sep 9, 2008
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7941 views

MCMC schemes for partially observed diffusions - Some recent advances
Aug 5, 2008
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3570 views

State estimation and prediction based on dynamic spike train decoding: noise, ad...
Aug 5, 2008
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3656 views

Approximate Bayesian computation: a simulation based approach to inference
Sep 9, 2008
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9633 views

Approximate inference for continuous time Markov processes
Sep 17, 2008
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5772 views

Information evolution of optimal learning
Sep 4, 2008
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5452 views