Course syllabus for Stochastic processes and Bayesian inference

The course syllabus contains changes
See changes

Course syllabus adopted 2019-02-22 by Head of Programme (or corresponding).

Overview

  • Swedish nameStokastiska processer och Bayesiansk inferens
  • CodeMVE550
  • Credits7.5 Credits
  • OwnerMPENM
  • Education cycleSecond-cycle
  • Main field of studyMathematics
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 20132
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0118 Examination 6 c
Grading: TH		 6 c
  • 09 Jan 2021 am J
  • 09 Apr 2021 am J
  • 23 Aug 2021 am J
0218 Written and oral assignments 1.5 c
Grading: UG		 1.5 c

In programmes

Examiner

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Course round 2

  • Teaching language English
  • Application code 99234
  • Maximum participants20
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0118 Examination 6 c
Grading: TH		 6 c
0218 Written and oral assignments 1.5 c
Grading: UG		 1.5 c

    Examiner

    Go to coursepage (Opens in new tab)

    Eligibility

    General entry requirements for Master's level (second cycle)
    Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

    Specific entry requirements

    English 6 (or by other approved means with the equivalent proficiency level)
    Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

    Course specific prerequisites

    A basic course in mathematical statistics

    Aim

    Building on a first course in mathematical statistics, this course is meant to give knowledge about a larger inventory of stochastic models, in particular stochastic processes, and greater knowledge about Bayesian inference, in particular in the context of these models. Together, this knowledge should give a solid basis for practical application and prediction using stochastic processes in connection with data analysis, and for further studies in statistics and probability theory.

    Learning outcomes (after completion of the course the student should be able to)

    After completion of the course the student should be able to use certain basic stochastic processes as models for real phenomena, and adapt these models using observed data. The student should be able to make predictions from the models, both using theoretical properties and using computer based simulation. The student should be able to make computer based inference using MCMC for certain simple models, and in general understand and apply the Bayesian inferential paradigm.

    Content

    Discrete time Markov chains. Branching processes. Basic principles for Bayesian inference, using discretization and conjugate priors. Hidden Markov models (HMM). Monte Carlo integration and Markov chain Monte Carlo (MCMC) simulation. Poisson processes. Time-continuous Markov chains. Introduction to Brownian motion.

    Organisation

    Lectures and excercise classes. Obligatory assignments.

    Literature

    Dobrow: Introduction to Stochastic Processes with R. (Available at Chalmers as e-book). Wiley 2016.

    Lecture Notes.

    Examination including compulsory elements

    Written exam. Obligatory assignments.

    The course syllabus contains changes

    • Changes to examination:
      • 2021-06-08: Examination datetime Examination datetime changed from 2021-08-21 Morning to 2021-08-23 Morning by Elisabeth Eriksson
        [2021-08-21 6,0 hec, 0118]
      • 2020-09-30: Grade raising No longer grade raising by GRULG
    • Changes to course rounds:
      • 2020-04-21: Block Block B added by lina haglund
        [Course round 1]
      • 2020-03-25: Block Block B added by lina haglund
        [Course round 1]