Monte Carlo methods for stochastic inference
Official Course Description
- LTH Course Description (SV)
- NF Course Description (SV)
- LTH Course Description (EN)
- NF Course Description (EN)
Simulation based methods of statistical analysis. Markov chain methods for complex problems, e.g. Gibbs sampling and the Metropolis-Hastings algorithm. Bayesian modelling and inference. The re-sampling principle, both non-parametric and parametric. The Jack-knife method of variance estimation. Methods for constructing confidence intervals using re-sampling. Re-sampling in regression. Permutations test as an alternative to both asymptotic parametric tests and to full re-sampling. Examples of mor complicated situations. Effective numerical calculations in re-sampling. The EM-algorithm for estimation in partially observed models.
- Spring, first half 2022 : Stand-alone Courses, Biomedical Engineering, Computer Science and Engineering, Engineering Physics, Industrial Engineering and Management, Master's Program in Mathematical Statistics, Engineering Mathematics