Student Pages
Postgraduate course

Stochastic Processes

  • ECTS credits10
  • Teaching semesterAutumn
  • Course codeSTAT220
  • Number of semesters1
  • LanguageEnglish
  • Resources

Main content

Teaching semester


Objectives and Content

The course will consider Markov processes in discrete and continuous time. The theory is illustrated with examples from operation research, biology and economy.

Learning Outcomes

After completed course, the students are expected to be able to:

  • Carry out derivations involving conditional probability distributions and conditional expectations.
  • Define basic concepts from the theory of Markov chains and present proofs for the most important theorems.
  • Compute probabilities of transition between states and return to the initial state after long time intervals in Markov chains.
  • Identify classes of states in Markov chains and characterize the classes.
  • Determine limit probabilities in Markov chains after an infinitely long period.
  • Derive differential equations for time continuous Markov processes with a discrete state space.
  • Solve differential equations for distributions and expectations in time continuous processes and determine corresponding limit distributions.

Recommended Previous Knowledge

40 ECTS in mathematics and statistics, including courses in calculus, linear algebra and basic statistics (MAT112 Calculus II, MAT121 Linear Algebra and STAT110 Basic Course in Statistics)

Compulsory Assignments and Attendance


Forms of Assessment

Written examination: 5 hours

Examination support materials: Non- programmable calculator, according to model listed in faculty regulations.

Examination only in the autumn.

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Exam information

  • For written exams, please note that the start time may change from 09:00 to 15:00 or vice versa until 14 days prior to the exam.

  • Type of assessment: Written examination (New exam)

    Withdrawal deadline
    Examination system
    Digital exam