Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FMS020F valid from Spring 2016

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  • The main aim lies in enhancing the scope of statistical problems that the doctoral student will be able to solve. The aim is also that the doctoral student shall gain proficiency with modern statistical methods for inference in partially observed stochastic processes. Partially observed processes encompass a broad class of statistical models with applications in, e.g. finance, environment, and biology. The last purpose of the course is to give the doctoral student knowledge and tools for both parameter inference in partially observed stochastic processes, and reconstruction of the unobserved parts of the process. Computational difficulties of the methods and possible solutions will be presented, allowing the students to apply the methods in their own research.
  • Inference and data imputation for diffusions and other continuos-time stochastic processes; iterated filtering; particle marginal methods for parameter inference; approximate Bayesian computation (ABC); inference for Gaussian Markov random fields.
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • Be able to describe the principles and methods for conducting inference for partially observed stochastic processes, with focus on continuous time or space domains.
    Be able to describe and highlight potential computational difficulties in inference.
    Be able to identify suitable inferential strategies depending on the given problem formulation and application area.
Competences and Skills
  • For a passing grade the doctoral student must
  • Be able to select and use a suitable inference strategy for the model and data at hand.
    Be able to implement software code for one or more inferential methods. Compare and discuss results.
    Be able to use the developed model for prediction.
    Present the analysis and conclusions of the analysis in a written report.
Judgement and Approach
  • For a passing grade the doctoral student must
  • Be able to describe differences between outcomes resulting from the use of exact and approximate inference strategies.
    Be able to reflect on the considered inference methods and their strengths and limitations for different applications.
Types of Instruction
  • Lectures
  • Exercises
  • Project
Examination Formats
  • Written report
  • Written assignments
  • To pass the course students must present the home assignments and an approved written project report.
  • Failed, pass
Admission Requirements
  • Basics of inference for stochastic processes, Bayesian methods and Monte Carlo methods (e.g. Markov Chain Monte Carlo, Metropolis-Hastings method). For example having taken the courses Time series analysis (FMS051/MASM17) and Monte Carlo and Empirical Methods for Stochastic Inference (FMS091/MASM11).
Assumed Prior Knowledge
Selection Criteria
  • The literature will consist of relevant key publications chosen by the lecturer(s).
Further Information
Course code
  • FMS020F
Administrative Information
  •  -12-21
  • FN1/AndersGustafsson

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