We develop a variational lower bound to the free energy for stochastic reaction models with monomolecular reactions which can be used for approximate inference. This bound is based... Watch Video

I will discuss two types of applications of an approximate inference technique (EC = expectation consistent) recently developed together with Ole Winther. The EC method is an extension... Watch Video

Continuous time Markov processes (such as jump processes and diffusions) play an important role in the modelling of dynamical systems in many scientific areas. In a variety of... Watch Video

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Optimal control for nonlinear stochastic dynamical systems requires thesolution of a nonlinear PDE, the so - called Hamilton Jacobi Bellman equation.Recently, Bert Kappen and Emanuel... Watch Video

Algorithms for approximate inference usually come without any guarantee for the quality of the approximation. Nevertheless, we often find cases where such algorithms perform extremely... Watch Video

There is a dramatic growth in the availability of complex data from a wide range of different applications. The challenge of the data analyzer is to extract knowledge from the raw... Watch Video

There is a dramatic growth in the availability of complex data from a wide range of different applications. The challenge of the data analyzer is to extract knowledge from the raw... Watch Video

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Probabilistic models explain complex observed data by a set of unobserved, hidden random variables based on the joint distribution of the variables. Statistical inference requires... Watch Video