Monte Carlo algorithms are widely used methods for solving problems that are otherwise intractable.These methods have had a profund impact in Statistics, Statistical Physics, Computer Science, Finance, Engineering and Reliability.  This methodology is fairly universal in that it formally applies to almost every settings.

The Workshop will cover both methodologies and applications. It will focus on adaptive Markov Chain Monte Carlo methods, population Monte Carlo algorithms, and on machine learning strategies on which the on-line optimisation of Monte Carlo algorithms heavily rely. Applications cover Mathematical Finance, Bayesian Statistics and Quantum Physics.

The Workshop will gather together graduate students and researchers interested in Monte Carlo methodology and its applications. It will emphasize new methods and mathematical problems that are motivated by contemporary developments in Monte Carlo methods. There will be 5 short courses (3h30), contributed sessions, and plenty of time in the interstices for informal discussion.

The number of participants is limited to 50 persons.