Course Syllabus for

Monte Carlo Methods for Statistical Inference
Monte Carlo-baserade statistiska metoder

FMS092F, 7.5 credits

Valid from: Spring 2014
Decided by: FN1/Anders Gustafsson
Date of establishment: 2014-01-13

General Information

Division: Mathematical Statistics
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: FMS091
Teaching language: English

Aim

The aim is that doctoral student shall gain proficiency with modern computer intensive statistical methods and use these to estimate quantities and parameters in complex models that arise in different applications (e.g. economics, signal processing, biology, climate, and environmental statistics). The purpose of the course is to give the doctoral student tools and knowledge to handle complex statistical problems and models in order to be able to use these in the doctoral student's own research. Further, the doctoral student should be able to assess the uncertainty of these estimates. The main aim lies in enhancing the scope of statistical problems that the doctoral student will be able to solve.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must Be able to identify and problemise the possibilities and limitations of statistical inference.

Course Contents

Simulation based methods of integration and statistical analysis. Monte Carlo methods for sequential problems. Markov chain methods, e.g. Gibbs sampling and the Metropolis-Hastings algorithm, for simulation and inference. Bayesian modelling and inference. The re-sampling principle, both non-parametric and parametric. Methods for constructing confidence intervals using re-sampling. Simulation based tests as an alternative to asymptotic parametric tests.

Course Literature

Sk├Âld, M.: Computer Intensive Statistical Methods.

Instruction Details

Types of instruction: Lectures, laboratory exercises, project

Examination Details

Examination format: Written report. Oral project presentation
Grading scale: Failed, pass
Examiner:

Admission Details

Course Occasion Information

Contact and Other Information

Course coordinator: Magnus Wiktorsson <magnus.wiktorsson@matstat.lu.se>


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