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FMS091/MASM11: Monte Carlo and Empirical Methods for Stochastic Inference

Current information for spring 2017.

General Information

The course has now changed course code to fmsn50/masm11. The new home page can be found HERE


Basic course in mathematical statistics and at least one of Stationary Stochastic Processes (FMS045/MAS210) or Markov processes (FMS180/MAS204)



Simulation-based methods of statistical analysis. Markov chain Monte Carlo methods for complex problems, e.g. Gibbs sampling and the Metropolis-Hastings algorithm. Bayesian modeling and inference. The resampling principle, both non-parametric and parametric. Methods for constructing confidence intervals using resampling. Resampling in regression. Permutations test as an alternative to both asymptotic parametric tests and to full resampling. Examples of more complicated situations. Effective numerical calculations in resampling. The EM-algorithm for estimation in partially observed models.

The course aim (in Swedish).


Examination consists of home assignments which will be handed out during the course.


Geof H. Givens and Jennifer A. Hoeting Computational Statistics Second Edition (2012) and some additional handouts. The book is available from KF-sigma.


Course administrator and lecturer (VT17)

Magnus Wiktorsson, MH:130
phone: 046-222 86 25
e-mail: magnusw@maths.lth.se